Next Article in Journal
A Three-Dimensional Enhanced Imaging Method on Human Body for Ultra-Wideband Multiple-Input Multiple-Output Radar
Next Article in Special Issue
Optimal Control of a Compact Converter in an AC Microgrid
Previous Article in Journal
A New Flicker Detection Method for New Generation Lamps Both Robust to Fundamental Frequency Deviation and Based on the Whole Voltage Frequency Spectrum
Previous Article in Special Issue
Three Topologies of a Non-Isolated High Gain Switched-Inductor Switched-Capacitor Step-Up Cuk Converter for Renewable Energy Applications
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Optimizing Generation Capacities Incorporating Renewable Energy with Storage Systems Using Genetic Algorithms

1
School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
2
Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
*
Author to whom correspondence should be addressed.
Electronics 2018, 7(7), 100; https://doi.org/10.3390/electronics7070100
Submission received: 23 May 2018 / Revised: 15 June 2018 / Accepted: 16 June 2018 / Published: 21 June 2018
(This article belongs to the Special Issue Renewable Electric Energy Systems)

Abstract

:
In grid advancement, energy storage systems are playing an important role in lowering the cost, reducing infrastructural investment, ensuring reliability and increasing operational capability. The storage system can provide stabilization services and is pivotal for backup power for emergencies. With a continuous rise in fuel prices and increasing environmental issues, the energy from renewable resources is gaining more popularity. The main drawbacks of some renewable sources are their intermittent energy generation and uncertain source availability, which has increased interest in energy storage systems (ESSs). This paper investigates the economic feasibility when ESSs are introduced in the electric grid with an expansion of a storage system as well as more percentage of the renewable energy integration and less percentage of fuel consumption by conventional power sources. The Artificial Neural Network is implemented to validate the forecasted load model. The uncertainties associated with the renewable energy system are handled by a chance-constrained model and solved by a genetic algorithm (GA) in MATLAB; selection criteria of GA for optimization process is also discussed in detail. The effectivity of the proposed methodology is verified by applying it to a case that lies in the western region of China.

1. Introduction

The idea of generation expansion planning problem (GEP) is implemented to realize the minimum cost plan for installing new power generation facilities so that the future electricity demands can be met. The plan incorporates decisions such as plant type, allocation, capacity, time of addition, and the consumption of each plant in the subsequent years. Each plan is proposed while keeping in consideration several constraints like demand, energy, and reliability. In the past few years, the requirement of energy storage systems (ESSs) has become critical and is expected to grow over the next decade [1]. Traditionally, only conventional generation utilities like hydro, nuclear and thermal power plants are considered in the capacity expansion planning process. With limited resources and increasing burden on the environment, there is a rise in the growth of renewable energy sources (RES). Although the addition of RES in the generation system offers many monetary and reliability benefits, the widespread use of RES by utility companies is still restricted. The main barrier which averts the large-scale use of RES is not the deficiency of adequate technology, but the higher cost of generated electricity [2]. Therefore, RES cannot compete with conventional energy resources only if the economics of both sources are taken into consideration. So apart from economics, other factors like the impact of both sources on the environment, or social benefits of both sources must be considered in the capacity expansion planning process which can promote large-scale renewable energy integration. The large-scale integration of RES with ESS will have a dynamic role in controlling the environmental problems related to conventional power plants; the research problem is dealing with the reduction in fuel consumption with more increase in RES in an economical way.
Recently, China has had a more aggressive approach in the renewable area in comparison with the rest of the world as the health of millions is affected by an increase of a large-scale pollution problem. With the continuous industrial development, GDP growth and rising export demands, the electricity sector must play an important role in China’s development. The installed capacity conventional power plants has been discussed in [3,4,5]. The electricity industry independently produced 4011 million tons of CO2 in China in 2011 [6] due to the significant contribution of conventional coal-fired power plants, making China the world’s biggest emitter of CO2 [7,8]. China planned to begin large-scale renewable energy integration with storage systems in capacity expansion planning, and there is enormous wind power potential in many provinces of China [9]. The negative impacts due to RES and ESSs may require a cost increase for maintaining the same level of reliability. It is significant to analyze these potential drawbacks to make sure that the effects on the benefits are minimal. There are many studies completed regarding ESSs with RES. Some planners devised deterministic models while other proposed stochastic models to simulate expansion planning [10,11]. Several mathematical and heuristic methodologies including linear programming, dynamic programming, decomposition methods, fuzzy logic, and particle swarm optimization simulated annealing, immune algorithms, and have been successfully applied to solve the problem [12,13,14,15,16,17,18,19,20]. Kannan and Slochanal compared different meta-heuristic techniques applied to this expansion problem [21]. The chance-constrained optimization method which is a branch of stochastic optimization method was used by M. Mazadi to solve the expansion problem without including renewable energy sources (RES) [22]. Multiple research studies have included RES in the planning process stage. Impact of renewable resources on the planning process was first considered by S.A. Farghal, but he also considered power storage devices to tackle the intermittent nature of renewable energy sources and this method has limited application for large-scale renewable energy integration [23]. Some planners have been considering distributed generation influence in their objective function [24,25,26,27,28]. In study [29], authors considered large-scale renewable energy integration in the capacity expansion planning problem, but the results showed that RES cannot compete with conventional energy sources on the economic basis. In 2013, the developments in several grid storage technologies has been presented in a report published by the U.S. Department of energy on Grid aspects of energy storage systems [30,31,32]. There has been a report discussing the operation and development of renewable energy system, with energy storage, particularly highlighting the challenges to synthesize resources with the operation and planning of the remaining power grid, including customer requirements, current generation resources and the transmission technology [33]. Another report published by the U.S. Department of Energy office discussed research focused on technologies that store electricity in chemicals or batteries [34]. Another study presents software implementation of an optimal power flow planning model; in this research a multi-period based optimization problem is formulated [35]. Several research studies on storage system with renewable energy systems are demonstrated in [36,37,38]. The feasibility of 40 MW Castle River wind farm with pumped hydro storage at Oldman dam is analyzed. The result presented an increase in profitability by factors of four when wind generation is integrated with storage systems [39]. In a research study for New Brunswick province [40], the authors considered the pumped hydroelectric energy storage system with wind energy. The results obtained from the study showed the reduction in generation cost of the system with an increase in wind integration level [39]. Owing to a broad difference in technology with reference to performance characteristics, some storage system technologies are more optimal for certain grid applications [41]. Some storage methods are also discussed in [42]. In studies [43,44], the energy storage technologies with their prime applications and challenges which they currently faced are given in detail. There are multiple projects which have been done regarding energy storage around the globe [41].
This paper presents a mathematical formulation of the chance-constrained programming model for an expansion problem. The model’s objective function, constraint functions and the effectiveness of the proposed model are discussed in detail. Furthermore, the genetic algorithm used to solve the model is explained along with its selection criteria for optimization process and significant benefits. The artificial neural network is implemented to validate the forecasted load model. The historical data needed for different predictors including holidays, weather etc. in the model are examined. Case studies are presented to show the efficacy of the proposed mathematical model. Economic evaluation, financial analysis and the comparative assessment of the different scenarios are achieved and presented in the research study.

2. Problem Formulation

The research study deals with generation expansion planning with renewable energy integration and energy storage system with the objective of the reduction of fuel-consuming power plants, a detailed financial analysis is performed with the following objectives. Minimizing the total cost of generation by introducing renewable energy systems and energy storage systems, formulating a chance-constrained programming model for capacity expansion planning that can deal with the intermittent nature of renewable energy sources. A case study is presented on a grid in West China (data from national key laboratory NCEPU) and the system’s financial reliability is studied.
The presented model is based on a generation capacity expansion algorithm and optimal storage system planning with the purpose of analyzing the interaction of renewable energy systems and energy storage in the grid. The optimization problem formulated is solved by using a heuristic optimization technique (genetic algorithm).

2.1. Objective Function

The proposed objective function aims to minimize the total cost. The cost represents the discounted present value of the amount which satisfies the demand for electricity during the planning period, considering the cost concerning storage systems. The cost components included in the objective function are: fuel cost, investment cost and operating and maintenance cost. The total cost to be minimized during the proposed planning period is given by the following equation:
C t o t a l = I + O + F
where, I = Investment Cost, F = fuel Cost, O = Operating cost. A brief description of all these cost components is as follows:

2.1.1. Investment Cost

The investment cost of new units in $/MWH is given by:
I = t = 1 T i ϵ N ( I i t · P i N · ω i t · C F i N · Δ t i N )
where, Iit is investment cost in $/MW, P i N is new power plant capacity of ith type in megawatts, ωit is the quantity of each unit type I needed in year t. Here, if the value of this discrete decision variable is 1, it means a unit capacity is added, C F i N gives unit type I capacity factor for new units, Δ t i N , represents the average utilization hours considered annually for each new unit type I, T time horizon (years), t time period (years), N set consisting of all the available generating technologies.
In this problem, energy storage and renewable energy are considered for initial investment plus conventional generating systems are installed to meet the load demand. Equation (3) presents the overall investment cost.
I = t = 1 T s ϵ S ( I t s · S s · ω s t · C F s · Δ t s ) + t = 1 T r ϵ R ( I t r · R r · ω r t · C F r · Δ t r ) + t = 1 T t r ϵ T R ( I t t r · T R t r · ω t r t · C F t r · Δ t t r )  
where, s is the energy storage type in set S consisting of available energy storage units, r is renewable generation type in set R consisting of available renewable generation type, Is investment cost for energy storage systems, Ir investment cost for renewable energy generators, I t t r is the investment cost of a thermal generating unit of type tr, wst, wrt are number of each unit type s or r needed in year t respectively, Ss capacity of storage system of types, Rr capacity of renewable generators of type r, CFs and CFr are capacity factor of type s and r generating technology, ∆ts and ∆tr are average utilization hours taken annually of unit type s and r respectively, T time horizon (years), t time period (years).

2.1.2. Fuel Cost

The fuel cost of existing and new power plants is given as:
F = t = 1 T i ϵ E ( F i t E · P i E · C F i E · Δ t i E )
For new power plants:
F = t = 1 T i ϵ N ( F i t N · P i N · ω i t · C F i N · Δ t i N )
where, E is a set consisting of all existing generating technologies and N is a set representing new generating units, P i E is the capacity of existing generating units of ith type, F i t E is representing the cost of fuel in $/MWH for existing generating unit type i in year t, C F i E is representing the capacity factor of existing unit type i, Δ t i E   is average utilization hours taken annually of existing power units. Here fuel cost of already existing thermal plants and new thermal plants are taken.
F = t = 1 T t r ϵ T R ( F t t r · T R t r · ω t r t · C F t r · Δ t t r )
tr denotes the type of thermal unit in available unit TR, TRtr is the capacity of thermal unit of type tr, wtrt are representing the quantity of each generating unit tr required in the year t, CFtr is the capacity factor of type tr generating technology, ∆ttr is the average utilization in hours taken annually of the unit type tr, F t t r is the fuel cost of thermal generating unit of type tr.

2.1.3. Operation and Maintenance Cost

The model representing the operation and maintenance cost of existing and new power plants are given as:
O = t = 1 T i ϵ N ( O i t N · P i N · ω i t · C F i N · Δ t i N )
where O i t N represents the operating and maintaining cost of new unit type i in $/MWH in t years.
O = t = 1 T i ϵ E ( O i t E · P i E · C F i E · Δ t i E )      
where O i t E represents the cost of operating and maintaining existing unit type i in $/MWH in years t.
Overall operation and maintenance cost is given by:
O = t = 1 T s ϵ S ( O t s · S s · ω s t · C F s · Δ t s ) + t = 1 T r ϵ R ( O t r · R r · ω r t · C F r · Δ t r ) + t = 1 T t r ϵ T R ( O t t r · T R t r · ω t r t · C F t r · Δ t t r )
The considered problem is formulated mathematically after combining these Equations (3), (6), and (9).
M i n c o s t = f ( I , O , F )
C o s t = t = 1 T s ϵ S ( I t s · S s · ω s t · C F s · Δ t s ) + t = 1 T r ϵ R ( I t r · R r · ω r t · C F r · Δ t r ) + t = 1 T t r ϵ T R ( I t t r · T R t r · ω t r t · C F t r · Δ t t r ) + t = 1 T t r ϵ T R ( F t t r · T R t r · ω t r t · C F t r · Δ t t r ) +   t = 1 T s ϵ S ( O t s · S s · ω s t · C F s · Δ t s ) + t = 1 T r ϵ R ( O t r · R r · ω r t · C F r · Δ t r ) + t = 1 T t r ϵ T R ( O t t r · T R t r · ω t r t · C F t r · Δ t t r )

2.2. System Modeling and Constraints

2.2.1. Energy Storage Cost Analysis

The factors included in the life cycle cost of the energy storage system are a capital cost, recharging energy cost and equipment replacement cost which is affected by storage system efficiency, system service life or life cycle cost. The present worth cost of the storage system is calculated considering its service life and inflation and discount rate factors. The present worth factor provides a methodology to represent payment for a given number of years; the cost is then leveled for the proposed period. Multiple references are used to study the current storage system trend and are utilized in the system cost analysis [45,46,47,48,49,50,51,52,53,54,55,56,57,58,59].

Methodology

Major components of the energy storage system contribute to overall cost are the storage unit ($/kWh), the power conversion unit ($/kW) and the charging source. The general form for capital cost calculation is given in Equation (12), Equation (13) is representing the proportionality of system rated power with power conversion equipment and Equation (14) shows proportionality of amount of energy stored with storage unit cost.
C o s t t o t a l ( $ ) = C o s t P c ( $ )   + C o s t s t o r a g e ( $ )
C o s t P c ( $ ) =   U n i t C o s t P c ( $ / kWh )   ×   P ( kW )
C o s t s t o r a g e ( $ ) = U n i t   C o s t s t o r a g e ( $ / kWh ) × E ( kWh )
Here, E = P × t , where E is the energy stored capacity, t is the storage time and P is the Power. Considering system inefficiency Equation (14) can be modified as follows with η representing efficiency. Equation (16) represents the capital cost considering power ratings.
C o s t s t o r a g e ( $ ) = U n i t   C o s t s t o r a g e ( $ / kWh ) × E ( kWh ) / η
C o s t s y s t e m   ( $ kW ) =   C o s t t o t a l / P
The present worth value of a given system cost is calculated using Equation (17) for the PW factor for a 5-year service life, economic assumptions used is presented in Table 1, where i represent the year, e is annual price escalating rate (%/year) and d = discount rate (%/year).
i = 1 5 ( 1   +   e ) i ( 1   +   d ) i
The costs in Figure 1a,b are based on certain standard assumptions considering different storage system category and their applications, and meant for comparative analysis. The number of cycles and round-trip efficiencies for a different system are presented in Figure 1c,d. Actual cost might vary and depend on many factors and the calculation method and assumption used here are under continuous debate among experts.
The result in Figure 2 shows that the present worth cost is depending in variable manner on different technologies and their storage durations (Storage duration 4 h, unless otherwise specified) Table 2 represents the storage duration, capacity of the system and their application detail. Long storage tends to require more storage capacity; similarly, frequent use is expensive and, hence, it reflects in the cost analysis in Figure 2. With present worth cost analysis it is possible to evaluate the benefits from different technology types in our study; we have used the average levelized cost 250 $/MWH for frequent discharge long storage system considering no replacement cost with the average life cycle of 14,000 and efficiency of 70–85% in a 5-year lifespan.

Energy Storage Constraint

The constraint representing charge and discharge power and amount of stored is given as follows:
0 S t s S m a x s
0 P t s , + P m a x s , +
0 P t s , P m a x s ,
The amount of energy stored should be in the range from nil to maximum stored capacity. The charging and discharging capacity of the storage system of types should be within a range from nil to their maximum charging and discharging capacity.

2.2.2. Renewable Energy Generators

R t r denotes the renewable energy generation from unit type r R during the time period t, R m a x r denotes the installed capacity of renewable generating system of type r.
R t r = r t r R m a x         r
Here, r r is a random variable representing renewable energy generation per unit generating capacity.

2.2.3. Annual Energy Demand Constraint

The annual energy generated from the combination of existing and new generating units must exceed the annual total demand for energy.
i N P i N · C F i N · Δ t i N · ω i t + i E P i E · C F i E · Δ t i E P L t · H t
where, P L t   represents Peak Load for the year t, H t represents utilization hours by load in the year t.

2.2.4. Power Generation Constraint

In order to satisfy the load and spinning reserve requirement by the generation capacity, the power generation constraint is formulated. Since the large-scale renewable energy integration with the storage system is considered (which brings intermittent power in the system), their availability rates are integrated into the constraint which makes certain that the generation is always surplus to the load requirements.
P r ( i N ( P i N · ω i t · ρ N ) + i E ( P i E · ρ E ) P L t ( 1 + S R t ) ) γ  
where, ρ N represents the availability rate of the new generation unit during peak load, ρ E represents the availability rate of the existing generation unit during peak load, S R t   represents the system spinning reserves requirement in year t and γ represents pre-defined power confidential level.

2.2.5. Upper and Lower Bond Constraint

This constraint is formulated to set a bound on the decision variable. This constraint describes that the number of units of a particular type should increase progressively throughout the planning horizon and each year the number of units of a particular type should be greater than the number of units of the same type in the preceding year. Similarly, the number of units of a particular type added in a particular year should not exceed the upper bound defined for that technology in that year.
ω m i n i ( t 1 ) ω i t ω m a x i ( t + 1 )
where, ω m i n i ( t 1 ) Number of units that had been underutilization at the beginning of t − 1 year, ω m a x i ( t + 1 ) Maximum units quantity that can be installed in the given area.

2.3. Genetic Algorithm

Capacity Expansion Planning models can be either deterministic or stochastic. The deterministic models are used when we have some pre-defined scenarios, while stochastic models find their application when uncertainties come into consideration. Mostly, deterministic techniques are used to solve the conventional expansion problem. When we deal with the systems, which considered large-scale integration of a renewable energy system, uncertainty comes into the system, and deterministic techniques are unable to solve the problem. Therefore, for the case of expansion planning considering higher percentage of RES, stochastic techniques are applied to solve large scale, non-linear optimization problems [61].
Genetic algorithm (GA) is a method to solve optimization problems based on a selection process. It starts by initializing a population of possible solution. Each candidate solution is then coded as a vector, termed a chromosome or genome. A fitness score of each chromosome is then calculated according to the defined objective. A probability of reproduction is assigned to each chromosome so that its chances of being selected in the next generation are proportional to its score relative to other chromosomes in the current population. The offspring of the next generation are generated by applying reproduction, crossover or mutation operator on the selected chromosome [62]. The process stops if a suitable solution is found, or if the available computing time is exceeded. Otherwise the new chromosomes are evaluated and the cycle repeats. The chances of obtaining a global optimal solution are quite high using GA and it requires great computational time.
The advantages of GA over conventional optimization techniques are as follows [62]:
  • GA does not use derivatives or other auxiliary data, the algorithm required only payoff information.
  • In comparison with conventional point-to-point methods, GA looks for solutions among populations of points, simultaneously works from a set of points and in parallel climbs many peaks, which leads to reduction in false peak finding probability.
  • GA utilizes probabilistic transition rules to guide a search toward the search space region with enhancement in payoff, while conventional optimization techniques use deterministic rules.
  • Instead of working with parameters, it works with a coding of parameter sets.

2.3.1. Solution Procedure Using GA

The fitness function is defined by Equations (1)–(9). The decision vector W t which is to be determined in the fitness function depends on following two factors:
  • Number of years in the planning horizon.
  • The type of technologies considered.
The length of the decision vector is determine by the product of number of years and number of technologies used in the planning horizon. For base case, having a 5-year planning horizon with five candidate plant, the state vector length would be 25. The fitness function can be represented by Equation (25).
m i n f ( ω 1 , ω 2 , ω 3 , ω 4 , ω 5 ) = k 1 ω 1 + k 2 ω 2 + k 3 ω 3 + k 25 ω 25
Here k represents the cumulative cost of a particular technology type.

2.3.2. Genome Structure

For the above example considered, a random genome can be modeled by Equation (26).
W t = ( ω 11 , ω 21 , ω 31 , ω 41 , ω 12 , ω 22 , ω 32 , ω 42 , , ω 15 , ω 25 , ω 35 , ω 45 )
where the first subscript denotes the type of technology and the second subscript de-notes the year in planning horizon. Alternatively, in matrix form where rows represent the number of years and columns represent the type of technologies, a particular element ω i t in the matrix represent the number of i type units required in year t.
W t = [ ω 1 , 1 ω 4 , 4 ω 5 , 1 ω 5 , 4 ]
The vector, W t , whose length is the number of variables in the problem, is a genome. The sum of all the elements of ith column represents the number of units of ith type required in the planning horizon.
The size of the population is set to 100, the population can be represented by a 100-by-15 matrix.
P o p u l a t i o n = [ ω 1 1 ω 15 1 ω 1 100 ω 15 100 ]
GA performs a series of computations, on each iteration, on the current population to generate a new population. Each successive population is termed as a new generation.

2.4. Operator Probabilities

Genetic algorithm uses the following three operators to produce children of next generation: selection; crossover; mutation. Population converges because of selection and crossover operators while mutation aids in maintaining diversity if early convergence or undue diversity occurs and the search becomes ineffective. The following GA parameters are set to get the solution: Population size (100); maximum number of generations (100); selection probability (0.02); crossover probability (0.4); mutation probability (0.04). The most troublesome section of chance-constrained programming is to make sure that the inequality constraints carrying the random variables are satisfied, to handle that a random simulation technology is applied. The power generation constraint as defined by Equation (23) is a chance constraint which holds the generation unit availability rate as a random variable.
P r { k i ( ω , ε ) 0 , i = 123 I } γ
Equation (29) shows a general inequality chance constraint in which   ω , ε represents the decision variable and random variable respectively.

2.5. Applied Algorithm

In the applied algorithm, the new generating units are considered as genes and the following steps are applied to reach the maximum number of generation sets.
  • The required data by algorithm is gathered.
  • Random population and genome feasibility testing is initialized.
  • Score evaluation of each genome in the population. In the current population, the genomes with best fitness function are taken as parents, which after applying different operators generate children of next generation either by mutation (making random changes to single parent) or by crossover (combing vector entries of pair of parents).
Repeat step 3, for new generation. The process stops when the maximum number of generations set is reached. The algorithm flow chart is given in Figure 3.

3. Data Descriptive Analysis

The focus of the model and algorithm developed is applied to actual storage expansion problem. The case is taken from a power company in West China. The power company has a combined capacity of 2000 MW consisting of thermal power plants, 400 MW of each three coal-fired plants, one natural gas combined cycle with the capacity of 300 MW and two gas-fired power plants 250 MW each. They have high capacity factors serving as a base load. In the case presented, we are analyzing the economic impact on power generation companies while incorporating the costs of future energy production technologies into the planning process. The case study covers a 5-year time span from 2016 to 2020 by considering the regional capacity data with no new installed capacity since 2016.
Base Case: In the basic case, the system does not contain any storage system. The current trend in providing energy to consumers is taken into consideration and economic analysis is carried out.
Proposed Case: In the proposed case scenario, storage system with renewable energy is integrated into the planning process and the economic change is analyzed. More contribution from renewable energy sources with the storage system is made.
The scope is limited to several technologies which are considered in the planning process:
  • Integrated gasification combined cycle (IGCC) power plant.
  • Natural gas combined cycle (NGCC) power plant.
  • Pulverized coal combustion (PC) power plant.
  • Solar power plant.
  • Wind power plant.
The power plants currently existing meeting the energy requirements are all fuel (coal, gas, oil) based such as:
  • Natural gas-fired power plant;
  • Coal-fired power plant;
  • Combined cycle natural gas-fired power plants.
The optimization model considers several unique features of each supply technology such as economic and operational specifications of each available plant and evaluates the optimal mix of supply sources to fulfill the energy requirements. The technical and financial data of the existing and new power plants are taken from the generation company.

3.1. Case Study Data

The required data is given in sections below.

3.1.1. Existing Power Plants

Table 3 shows the financial and technical data associated with existing power plants.

3.1.2. New Power Plants

The model determines the number of each type of new plants needed to fulfill the energy requirements along with the time horizons. The generating technologies considered in the storage planning process are as follows:
  • Natural gas combined cycle (NGCC) power plant.
  • Integrated gasification combined cycle (IGCC) power plant.
  • Pulverized coal combustion (PC) power plant.
  • Solar power plant (with storage system).
  • Wind power plant (with storage system).
In addition to the above-mentioned technologies, other technologies can also be considered but the scope of this paper is limited to the above-mentioned technologies. All considered power plants have distinctive characteristics and vary greatly from each other in terms of economic and operational parameters. Some technologies require high capital costs while other technologies require lower capital costs but have high fuel rates and, thus, high generating costs are associated with them. In terms of capital cost, construction of large capacity power plants over smaller ones is considered favorable economically. The operation of two smaller capacity units is expensive as compared to a single large unit having the same generating capacity. The financial and technical data of the considered power plants is given in Table 4 [63,64,65,66].

3.2. Data for Load Forecast

The forecasting of future energy requirements is achieved using statistical techniques to ensure better expansion planning [67]. Long-term load forecasting of one generation company which resides in the western region of China has been considered, based on the increasing industrialization and rapid growth in gross domestic product (GDP). In particular, the forecast results of annual energy demand, annual peak load demand, and annual load utilization hours are considered. The forecasting is performed in MATLAB using ANN toolbox considering GDP, regional temperature and holidays as predictors. The model is validated with best validation performance error of 2.08% at epoch 107, where similar paths are followed by the test set and validation set’s error shown in Figure 4. Table 5 demonstrates the load forecast results for the planning area.

3.3. Assumptions

The following assumptions have been made for the research study:
  • There is no connection between the average power price and total power demand. Furthermore, selection of individual supply technologies is influenced by their relative prices and emissions.
  • Since the study considers the large-scale integration of the RES with a storage system, as RES generates random and intermittent power, the system’s spinning reserve requirement is assumed to be 20%.
  • Fuel and fixed O&M costs of all power generation facilities are supposed to stay constant throughout the planning duration, and the number of units per technology should increase in the coming year.
  • A discount rate of 10% is considered for the present study.
  • The case study done is according to installed capacity, not according to allocated power.

4. Results and Discussions

4.1. Base Case

This scenario considers that the on-going strategy of power production is continued i.e., no energy storage units are installed in the power system. This case study further assumes that existing generation capacity continues to meet the load demand along with the new installed capacities in the current system. Figure 5 describes the installation scenario of different kind of power plants in the base case.
The contribution of renewable energy is only 13% in this case. NGCC plants contributed to 30%, owing to their low emissions, high efficiency and cheap operation shown in Figure 6. Fossil fuel-fired power plants have a high capacity factor and average annual utilization hours, and their capital cost is relatively below wind and solar units; therefore, they contribute significantly in this case. Since solar panels have very high capital costs, their total installed capacity is limited to just 1800 MW.
The results indicate that fossil fuel-fired power plants continue to dominate the power generation industry. Due to the poor capacity factor of renewable energy sources and intermittent power generation, their contribution was reduced to a small proportion. Power plants utilizing fossil fuels produce expensive electricity and generate hazardous pollutants, however, they still take lead on renewable energy generation sources. Although the electricity generated in this case is cheap, the environmental damage to the ecosystem is costly [68].

Financial Analysis Base Case

Investment, operation, maintenance and fuel costs are considered in this section. We have the highest fuel cost in our case which is 11,063.9 million dollars, with 9164.1 million dollars of investment, having 1523.01 million dollars of operation and maintenance cost as baseload plants were mostly installed. Total expansion without energy storage cost up to 21,751.01 million dollars, with an average of 0.65 $/kWh, the analysis is given in Table 6. The cost for environmental damage must be considered, though the electricity produced in this case is inexpensive.
Cost analysis of different technologies is given in Figure 7. NGCC units make the most of the cost in each year in the base case, the capital cost related to renewable energy units is more, and a lower number of renewable energy generators is installed. A summary of base case financial and load analysis is given in Table 7.

4.2. Proposed Case

In this scenario, a storage system with renewable energy is integrated into the planning process and the economic changes are analyzed with external cost taken into account. More contribution from renewable energy sources with storage system is made in order to control the fuel and external gas emission. A brief explanation of external cost is given, and overall cost is compared with the base case.
In each year, an increasing number of wind power plants are selected as compared to IGCC and NGCC power plants but still the major contribution of the total cost during each year is caused by NGCC power plants as shown in Figure 8. This shows that the operation of RES is quite cheap compared with costs of fossil fuel power plants. The clean energy contribution in the proposed case is increased from 13 to 39 percent and gas a 35.6 percent decrease in fossil fuel-fired power plants as shown in Figure 9 and Figure 10.

Financial Analysis Proposed Case

Total expenditure is 23,198.9 million dollars, with a fuel cost of 9519.94 million dollars, investment cost of 12,622.51 million dollars and O&M cost of 1003.84 million dollars. Electricity generated in this scenario was 0.67 cent $/kWh expensive than that of the base scenario because the storage system associated with the electricity generation was considered in this scenario. Table 8 gives the detailed total analysis of existing and new power plants installed. A summary of the proposed case is given in Table 9. The storage system is making 36.4% of the total cost in a five-year planning horizon with an increase in total cost of 6.67% from base case cost which if further analyze for the long term may be further reduced. The result shows that the total cost while utilizing a storage system is respectively high.

4.3. Comparison of Case Study

In Table 10, a comparison of two cases is given in different terms. The comparison of these two case studies suggests that the costs of energy in the capacity expansion program serves as an important policy factor for large-scale integration of clean energy systems as the share of clean sources increases from 13% in the base case to 39% in the proposed case. The average cost of electricity generated is almost comparable with a 35.6 percent decrease in fuel cost and CO2 emission. The overall fuel cost is decreased with an increase in renewable energy integration.
The total cost analysis with the storage system and without storage system is shown in Figure 11.
The storage system is making 36.4% of the total cost in a five-year planning horizon with an increase in total cost of 6.67% from base case cost.

5. Conclusions

Renewable energy systems have poor capacity factors and availability rates, are intermittent in nature and are costly. Hence, they cannot compete with conventional fossil fuel-fired power plants. The focus of research here is to increase the use of clean energy with large-scale RES integration, which leads to lowering of fuel usage by conventional power plants. This has a counter effect on less fuel consumption and less emission of hazardous gases. The economic feasibility is evaluated when ESSs are introduced in the electric grid with an expansion of a storage system as well as more percentage of the renewable energy integration and less percentage of fuel consumption by conventional power sources. The artificial neural network is used to validate the forecasted load model with historical weather and holidays as input predictors. The uncertainties associated with the renewable energy system are handled by a chance-constrained model, and solved by a genetic algorithm (GA) in MATLAB. The results suggested an increase in proportion of clean energy from 13 to 39%, leading to a sharp drop in CO2 emissions to 35.6%, thereby reducing the devastating effect on the environment. The result revealed that for the betterment of the environment, the role of the storage system is critically significant to renewable energy integration. If the financial assessment of the used grid data is analyzed by considering gas-related external costs, it would produce more promising results toward adoption of storage and renewable energy systems.

Author Contributions

F.A. and S.H. have contributed to the conceptualization behind this work. F.A. has prepared the write up and manuscript. D.F. has helped with modelling and validation. S.H. has helped with the write up, sectionalizing and appropriate referencing. This work had been jointly supervised by D.F. and Z.Y.

Funding

This research received no external funding.

Acknowledgments

I am extremely thankful to Yingyun Sun form National Electric Power Key laboratory North China Electric Power University (NCEPU) for providing necessary information and guidance to pursue this research. I also place my sense of gratitude to one and all, who directly or indirectly involved in this venture.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Design News. Available online: https://www.designnews.com/automation-motion-control/beyond-smart-grid-utilities-will-still-need-electric-storage/73173626538606 (accessed on 9 September 2013).
  2. Kothari, R.P.; Kroese, D.P. Optimal generation expansion planning via the cross-entropy method. In Proceedings of the Winter Simulation Conference, Austin, TX, USA, 13–16 December 2009. [Google Scholar]
  3. Huang, Q. The Development Strategy for Coal-Fired Power Generation in China. Cornerstone Mag. 2016. Available online: http://cornerstonemag.net/the-development-strategy-for-coal-fired-power-generation-in-china/ (accessed on 1 May 2017).
  4. Hong, S.; Cosbey, A.; Savage, M. China’s Electrical Power Sector, Environmental Protection and Sustainable Trade; International Institute for Sustainable Development: Winnipeg, MB, Canada, 2009. [Google Scholar]
  5. International Energy Agency. Revised Yearly, the IEA’s Reference Scenario Projects Supply and Demand for Oil, Gas, Coal, Renewable Energy, Nuclear Power, Electricity, and Related Carbon Dioxide Emissions to the Year 2030 for 21 Regions and the World as a Whole; International Energy Agency: Paris, France, 2017.
  6. International Energy Agency. Annual 2011 CO2 Emissions; International Energy Agency: Paris, France, 2011.
  7. U.S. Energy Information Administration-EIA-Independent Statistics and Analysis. U.S. Energy Information Administration (EIA), 2017. Available online: https://www.eia.gov/ (accessed on 1 May 2018).
  8. Key Findings, WEO 2017. Available online: https://www.iea.org/weo2017/ (accessed on 1 January 2018).
  9. Li, J. China Wind Energy Outlook. GWEC|Representing the Global Wind Energy Industry. 2012. Available online: http://www.gwec.net/wp-content/uploads/2012/11/China-Outlook-2012-EN.pdf (accessed on 5 May 2013).
  10. Mo, B.; Hegge, J.; Wangensteen, I. Stochastic generation expansion planning by means of stochastic dynamic programming. IEEE Trans. Power Syst. 1991, 6, 662–668. [Google Scholar] [CrossRef]
  11. Park, Y.M.; Park, J.B.; Won, J.R. A hybrid based Genetic Algorithm/dynamic programming approach to optimal long-term generation expansion planning. Int. J. Electr. Power Energy Syst. 1998, 20, 295–303. [Google Scholar] [CrossRef]
  12. Climaco, J.; Antunes, C.H.; Martins, A.G.; Almeida, A.T. A multiple objective linear programming model for power generation expansion planning. Int. J. Energy Res. 1995, 19, 419–432. [Google Scholar] [CrossRef] [Green Version]
  13. Dantzig, G.; Glynn, P.; Avriel, M.; Stone, J.; Entriken, R.; Nakayama, M. Decomposition Techniques for Multi-Area Generation and Transmission Planning under Uncertainty; Final Report; Electric Power Research Inst.: Palo Alto, CA, USA, 1989; p. 121. [Google Scholar]
  14. David, A.K.; Zhao, R. An expert system with fuzzy sets for optimal planning (of power system expansion). IEEE Trans. Power Syst. 1991, 6, 59–65. [Google Scholar] [CrossRef]
  15. Kannan, S.; Slochanal, S.M.R.; Subbaraj, P.; Padhy, N.P. Application of particle swarm optimization technique and its variants to generation expansion planning problem. Electr. Power Syst. Res. 2004, 70, 203–210. [Google Scholar] [CrossRef]
  16. Chen, S.-L.; Zhan, T.-S.; Tsay, M.-T. Generation expansion planning of the utility with refined immune algorithm. Electr. Power Syst. Res. 2006, 76, 251–258. [Google Scholar] [CrossRef]
  17. Park, J.-B.; Park, Y.-M.; Won, J.-R.; Lee, K.Y. An improved genetic algorithm for generation expansion planning. IEEE Trans. Power Syst. 2000, 15, 916–922. [Google Scholar] [CrossRef]
  18. Yildirim, M.; Erkan, K.; Ozturk, S. Power generation expansion planning with adaptive simulated annealing evolutionary based Genetic Algorithm. Int. J. Energy Res. 2006, 30, 1188–1199. [Google Scholar] [CrossRef]
  19. Sirikum, J.; Techanitisawad, A. Power generation expansion planning with emission control: A nonlinear model and a GA-based heuristic approach. Int. J. Energy Res. 2006, 30, 81–99. [Google Scholar] [CrossRef]
  20. Tanabe, R.; Yasuda, R.Y.K. Practical method for generation expansion planning based on dynamic programming. Electr. Eng. Jpn. 1992, 112, 114–127. [Google Scholar] [CrossRef]
  21. Kannan, S.; Slochanal, S.; Padhy, N. Application and comparison of meta-heuristic techniques to generation expansion planning problem. IEEE Trans. Power Syst. 2005, 20, 466–475. [Google Scholar] [CrossRef]
  22. Liu, B. Uncertainty Theory, 2nd ed.; Spring: Berlin/Heidelberg, Germany, 2007. [Google Scholar]
  23. Farghal, S.A.; Roshdy, M.; Abdel, A. Generation Expansion planning Including Renewable Energy Source. IEEE Trans. Power Syst. 1988, 3, 1277–1283. [Google Scholar] [CrossRef]
  24. Tanabe, R.; Yasuda, K.; Yokoyama, R.; Sasaki, H. Flexible Generation Mix under Multi Objectives and Uncertainties. IEEE Trans. Power Syst. 1993, 8, 581–587. [Google Scholar] [CrossRef]
  25. David, M.R.; Stephen, T.L.; John, C.K. Expansion Planning with Dispersed Fuel Cell Power Plants on Actual Utility Systems. IEEE Trans. Power Appar. Syst. 1984, PAS-103, 2388–2394. [Google Scholar]
  26. Caramants, M.C.; Tabors, R.D.; Nochur, K.S. The Introduction of Non dispatch able Technologies as Decision Variables in Long-term Generation Expansion Models. IEEE Trans. Power Appar. Syst. 1982, PAS-101, 2658–2668. [Google Scholar] [CrossRef]
  27. Wang, P.; Billinton, R. Reliability assessment of a restructured power system using reliability network equivalent techniques. IEEE Proc. Gener. Transm. Distrib. 2003, 150, 555–560. [Google Scholar] [CrossRef]
  28. Michael, C. Analysis of Non-dispatch able Options in the Generation Expansion Plan. IEEE Trans. Power Appar. Syst. 1983, PAS-102, 2098–2104. [Google Scholar]
  29. Sun, Y.; Han, T.; Ashfaq, A. A Chance-Constrained Programming Based Renewable Resources Included Generation Expansion Planning Method and Its Application. In Proceedings of the 2012 IEEE Asia-Pacific Power and Energy Engineering Conference (APPEEC), Shanghai, China, 27–29 March 2012. [Google Scholar]
  30. Hostick, D.; Belzer, D.B.; Hadley, S.W.; Markel, T.; Marnay, C.; Kintner-Meyer, M. End-Use Electricity Demand; Vol. 3 of Renewable Electricity Futures Study; NREL/TP-6A20-52409-3; National Renewable Energy Laboratory: Golden, CO, USA, 2012.
  31. IMS Research. IMS Research (Now Owned by IHS-CERA) Report ‘The Role of Energy Storage in the PV Industry–World–2013 Edition’; IMS Research: Wellingborough, UK, 2013. [Google Scholar]
  32. U.S. Department of Energy and Strategic Planning. Available online: http://energy.gov/sites/prod/files/2011_DOE_Strategic_Plan_.pdf (accessed on 5 September 2012).
  33. IEC MSB (Market Strategy Board), Grid Integration of Large-Capacity Renewable Energy Sources and Use of Large-Capacity Electrical Energy Storage, White Paper, October 2012. Available online: http://www.iec.ch/whitepaper/pdf/iecWP-gridintegrationlargecapacity-LR-en.pdf (accessed on 24 June 2015).
  34. U.S. Department of Energy Office of Electricity Delivery and Energy Reliability. Energy Storage, Program Planning Document; U.S. Department of Energy Office of Electricity Delivery and Energy Reliability: Washington, DC, USA, February 2011.
  35. Hu, Z.; Jewell, W.T. Optimal generation expansion planning with integration of variable renewables and bulk energy storage systems. In Proceedings of the 2013 1st IEEE Conference on Technologies for Sustainability (SusTech), Portland, OR, USA, 1–2 August 2013. [Google Scholar]
  36. Yang, P.; Nehorai, A. Hybrid energy storage and generation planning with large renewable penetration. In Proceedings of the 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), St. Martin, France, 15–18 December 2013. [Google Scholar]
  37. Yang, P.; Nehorai, A. Joint Optimization of Hybrid Energy Storage and Generation Capacity with Renewable Energy. IEEE Trans. Smart Grid 2014, 5, 1566–1574. [Google Scholar] [CrossRef]
  38. Manwell, J.F.; McGowan, J.G.; Rogers, A.L. Wind Energy Explained, Theory, Design and Application; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2009. [Google Scholar]
  39. Kapsali, M.; Kaldellis, J.K. Combining Hydro and Variable Wind Power Generation by Means of Pumped-Storage under Economically Viable Terms. Appl. Energy 2010, 87, 3475–3485. [Google Scholar] [CrossRef]
  40. Ea Energy Analysis. Large Scale Wind Power in New Brunswick-A Regional Scenario Study Towards 2025; Prepared for New Brunswick System Operator and New Brunswick Department of Energy; Ea Energy Analysis: Hillerød, Denmark, August 2008. [Google Scholar]
  41. Technology Information. Technology Information|ClimateTechWiki. Available online: http://www.climatetechwiki.org/technology-information (accessed on 1 September 2016).
  42. DOE Global Energy Storage Database. DOE Global Energy Storage Database. 2016. Available online: http://www.energystorageexchange.org/ (accessed on 1 November 2017).
  43. Technical Summaries. Energy Frontier Research Centers, Argonne National Laboratory, Argonne Scientific Publications|Argonne National Laboratory. 2010. Available online: http://www.cse.anl.gov/iact/publications/EFRC_technical_summaries_Jan_2010.pdf (accessed on 1 May 2017).
  44. Renewable Power Stations. Arista Power. 2016. Available online: https://aristapower.com/portable-power/renewable-power-stations/ (accessed on 1 May 2017).
  45. Susan, S.; James, E. Benefit/Cost Framework for Evaluating Modular Energy Storage; SAND2008-0978; Sandia National Laboratories: Livermore, CA, USA, 2008.
  46. Eyer, J.M.; Iannucci, J.J.; Corey, G.P. Energy Storage Benefits and Market Analysis Handbook; SAND2004-6177; Sandia National Laboratories: Livermore, CA, USA, 2004.
  47. California Energy Commission and the Public Interest Energy Research Program, Electric Energy Storage Demonstration Projects in California, Request for Proposals (RFP) #500-03-501. Attachment 14: Electric Energy Storage Benefits and Market Analysis. 2003. Available online: http://www.energy.ca.gov/contracts/RFP_500-03-501-07-31_RFP_500-03-501.PDF (accessed on 24 June 2015).
  48. Schoenung, S.M.; Hassenzahl, W.V. Long- vs. Short-Term Energy Storage Technologies Analysis: A Life-Cycle Cost Study; SAND2003-2783; Sandia National Laboratories: Livermore, CA, USA, 2003.
  49. Schoenung, S.M.; Hassenzahl, W.V. Long vs. Short-Term Energy Storage: Sensitivity Analysis; SAND2007-4253; Sandia National Laboratories: Livermore, CA, USA, 2007.
  50. Schoenung, S.M. Characteristics and Technologies for Long- vs. Short-Term Energy Storage; SAND2001-0765; Sandia National Laboratories: Livermore, CA, USA, 2001.
  51. Raster, D. Overview of Electric Energy Storage Options for the Electric Enterprise; EPRI Presentation to the California Energy Commission; Electric Power Research Institute: Palo Alto, CA, USA, February 2009. [Google Scholar]
  52. Nourai, A. Installation of the First Distributed. Energy Storage System (DESS) at American Electric Power (AEP); SAND2007-3580; Sandia National Laboratories: Livermore, CA, USA, 2007.
  53. Energy Insights. Summary of ARRA-Funded Energy Storage Projects; Energy Insights: Houston, TX, USA, March 2010. [Google Scholar]
  54. Electricity Advisory Committee. Bottling Electricity: Storage as a Strategic Tool for Managing Variability and Capacity Concerns in the Modern Grid; Electricity Advisory Committee: Washington, DC, USA, December 2008.
  55. Zhou, Y. The Security Implications of Chinas Nuclear Energy Expansion. Nonprolif. Rev. 2010, 17, 347–363. [Google Scholar] [CrossRef]
  56. Applications of Energy Storage Technology. Applications of Energy Storage Technology|Energy Storage Association. 2016. Available online: http://energystorage.org/energy-storage/applications-energy-storage-technology (accessed on 1 September 2016).
  57. Clark, N.; (Sandia National Laboratories, Livermore, CA, USA). Personal communication, March 2010.
  58. Eyer, J.; Corey, G. Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide; SAND2010-0815; Sandia National Laboratories: Livermore, CA, USA, 2010.
  59. Sayeef, S.; Heslop, S.; Cornforth, D.; Moore, T.; Percy, S.; Ward, J.K.; Berry, A.; Rowe, D. Solar Intermittency: Australia’s Clean Energy Challenge, Characterising the Effect of High Penetration Solar Intermittency on Australian Electricity Networks; CSIRO: Canberra, Australia, 2012; Available online: https://publications.csiro.au/rpr/download?pid=csiro:EP121914&dsid=DS1 (accessed on 8 April 2015).
  60. Raslter, D.; Akhil, A.; Gauntlett, D.; Cutter, E. Energy Storage System Cost 2016 Update Executive Summary. Electric Power and Research Institute, 2016. Available online: https://www.epri.com/#/pages/product/000000003002012046/?lang=en (accessed on 8 June 2017).
  61. Peter, K.; Stein, W. Stochastic Programming; John Wiley & Sons: Chichester, UK, 1994. [Google Scholar]
  62. Zhu, J.; Chow, M. A review of emerging techniques on generation expansion planning. IEEE Trans. Power Syst. 1997, 12, 1722–1728. [Google Scholar]
  63. US Energy Information Administration (EIA) of the U.S. Department of Energy (DOE). Levelized Cost of New Generation Resources in the Annual Energy Outlook 2013; Report of the US Energy Information Administration (EIA) of the U.S. Department of Energy (DOE); US Energy Information Administration (EIA) of the U.S. Department of Energy (DOE): Washington, DC, USA, 2013.
  64. EIA (Energy Information Administration). Assumptions for the Annual Energy Outlook 2003 with Projections to 2050; Energy Information Administration, Office of Integrated Analysis and Forecasting, U.S. Department of Energy (DoE): Washington, DC, USA, 2003.
  65. Wu, Z.; Delaquil, P.; Larson, E.; Chen, W.; Gao, P. Future implications of Chinas energy-technology choices: summary of a report to the Working Group on Energy Strategies and Technologies. Energy Sustain Dev. 2001, 5, 19–31. [Google Scholar]
  66. Mittal, M.L.; Sharma, C.; Singh, R. Estimates of Emissions from Coal Fired Thermal Power Plants in India. In Proceedings of the International Emission Inventory Conference, Tampa, FL, USA, 13–16 August 2012. [Google Scholar]
  67. Al-Alawi, S.M.; Islam, S.M. Principles of electricity demand forecasting. I. Methodologies. Power Eng. J. 1996, 10, 139–143. [Google Scholar] [CrossRef]
  68. Externalities of Energy ‘Externe’ Project, Method for Estimation of Physical Impacts and Monetary Valuation for Priority Impact Pathways, Extrene. 2006. Available online: http://www.externe.info/externe_2006/reportex/vol2.pdf (accessed on 1 April 2015).
Figure 1. Cost and performance assumptions: (a) Power subsystem cost for different storage technologies; (b) Energy Storage subsystem cost for different storage technologies; (c) Number of cycles for different storage technology types; (d) Round trip efficiency for different storage technology types [60].
Figure 1. Cost and performance assumptions: (a) Power subsystem cost for different storage technologies; (b) Energy Storage subsystem cost for different storage technologies; (c) Number of cycles for different storage technology types; (d) Round trip efficiency for different storage technology types [60].
Electronics 07 00100 g001aElectronics 07 00100 g001b
Figure 2. Present worth Cost of 5-year Operation in Year 1 ($/kW).
Figure 2. Present worth Cost of 5-year Operation in Year 1 ($/kW).
Electronics 07 00100 g002
Figure 3. Applied algorithm flow chart.
Figure 3. Applied algorithm flow chart.
Electronics 07 00100 g003
Figure 4. Model Validation Plot.
Figure 4. Model Validation Plot.
Electronics 07 00100 g004
Figure 5. Base case unit installed scenario.
Figure 5. Base case unit installed scenario.
Electronics 07 00100 g005
Figure 6. Percentage of the unit type utilized.
Figure 6. Percentage of the unit type utilized.
Electronics 07 00100 g006
Figure 7. Cost analyses of different technologies.
Figure 7. Cost analyses of different technologies.
Electronics 07 00100 g007
Figure 8. Proposed case unit installed scenario with a planning horizon.
Figure 8. Proposed case unit installed scenario with a planning horizon.
Electronics 07 00100 g008
Figure 9. Percentage of installed capacity of each unit type in proposed case.
Figure 9. Percentage of installed capacity of each unit type in proposed case.
Electronics 07 00100 g009
Figure 10. Cost analysis of each unit type proposed case.
Figure 10. Cost analysis of each unit type proposed case.
Electronics 07 00100 g010
Figure 11. Case analysis with and without storage system.
Figure 11. Case analysis with and without storage system.
Electronics 07 00100 g011
Table 1. Assumptions for cost analysis [60].
Table 1. Assumptions for cost analysis [60].
ParametersValue
Fuel Cost$5/MBTU
Electricity Cost for Charging10¢/kWh
Customer Fixed Charge Rate15%
Utility Fixed Charge Rate11%
Inflation Rate2%
Discount Rate10%
Service life5 years
Table 2. Technology and Application type.
Table 2. Technology and Application type.
TypeStorage DurationCapacityFunction
Long storage duration, frequent discharge4–81 cycle/day × 250 days/yearLoad-leveling, source-following, arbitrage
Long storage duration, infrequent discharge4–820 times/yearCapacity credit
Short storage duration, frequent discharge0.25–14 × 15 min of cycling × 250 days/year= 1000 cycles/year Frequency or area regulation
Short storage duration, infrequent discharge0.25–120 times/yearPower quality, momentary carry-over
Table 3. Specifications of Existing Power Plant.
Table 3. Specifications of Existing Power Plant.
Type (Power Plant)Capacity (MW)O&M Cost ($/MWH)Fuel Cost ($/MWH)Capacity FactorEfficiency
NGCC3002.349.40.350.30
Natural gas fired 5003.5830.750.45
Coal fired12004.832.20.850.36
Table 4. New power plant cost detail.
Table 4. New power plant cost detail.
Type (Power Plant)Per Unit Capacity (MW)Levelized Capital Cost ($/MWH)Fuel Cost ($/MWH)O&M Cost ($/MWH)Capacity FactorEfficiency
Storage system1002500.01.30.70–0.900.70–0.85
Wind power plant10070.30.013.10.340.30
Solar power plant100130.40.09.90.250.40
PC combustion power plant10065.729.24.10.850.36
IGCC power plant10088.437.28.80.850.42
NGCC power plant10017.448.41.70.870.51
Table 5. Load Forecast of Planning Area.
Table 5. Load Forecast of Planning Area.
Year20162017201820192020
Energy Demand (Annual-GWH)32,20440,24346,62153,45961,459
Annual load utilization hours57055940617564106645
Annual peak load demand (MW)56456775755083409249
Table 6. Total Cost Analysis of Unit Type in Base Case.
Table 6. Total Cost Analysis of Unit Type in Base Case.
Years20162017201820192020
Existing Plant cost (M$)840.04840.04840.04840.04840.04
New plant cost (M$)2510.13235.73561.74043.34497.7
Total cost (M$)3350.144075.744401.744883.345337.74
Table 7. Annual Summary Base Case.
Table 7. Annual Summary Base Case.
Case StudyBase Case
Years20162017201820192020
Annual Peak Load Demand (MW)56456775755083409249
Annual Energy Produced (GWH)49,450.259,34966,13873,058.481,021.24
Total Installed Capacity (MW)60007100780086009500
Annual Expenditure (M$)33504075.64401.74883.45337.7
Average Electricity Cost (cents/kWh)6.776.876.666.686.59
Table 8. Total Cost Analysis of Unit Type in Proposed Case.
Table 8. Total Cost Analysis of Unit Type in Proposed Case.
Year20162017201820192020
Existing Plant cost (M$)840.04840.04840.04840.04840.04
New plant cost (M$)2840.6653501.463814.164208.7574633.657
Total cost (M$)3680.7054341.54654.25048.85473.7
Table 9. Annual Summary—Proposed Case.
Table 9. Annual Summary—Proposed Case.
Case StudyProposed Case
Years20162017201820192020
Annual Peak Load Demand (MW)56456775755083409249
Annual Energy Produced (GWH)49,450.259,34966,13873,058.48,1021.24
Total Installed Capacity (MW)60007100780086009500
Annual Expenditure (M$)3680.74341.54654.25048.85473.7
Average Electricity Cost (cents/kWh)7.447.327.046.916.76
Table 10. Cases Comparison.
Table 10. Cases Comparison.
TermsBase CaseProposed Case
Total Expenditure (million $)21,751.0123,198.9
Fuel Cost (million $)11,063.99519.94
Clean Energy Contribution (%)1339
Average Cost of Electricity (cents $/kWh)6.596.74
CO2 Emission (Thousand Tons)109,06638,827

Share and Cite

MDPI and ACS Style

Abbas, F.; Habib, S.; Feng, D.; Yan, Z. Optimizing Generation Capacities Incorporating Renewable Energy with Storage Systems Using Genetic Algorithms. Electronics 2018, 7, 100. https://doi.org/10.3390/electronics7070100

AMA Style

Abbas F, Habib S, Feng D, Yan Z. Optimizing Generation Capacities Incorporating Renewable Energy with Storage Systems Using Genetic Algorithms. Electronics. 2018; 7(7):100. https://doi.org/10.3390/electronics7070100

Chicago/Turabian Style

Abbas, Farukh, Salman Habib, Donghan Feng, and Zheng Yan. 2018. "Optimizing Generation Capacities Incorporating Renewable Energy with Storage Systems Using Genetic Algorithms" Electronics 7, no. 7: 100. https://doi.org/10.3390/electronics7070100

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop