A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources
Abstract
:1. Introduction
2. Structure of Smart Grids
2.1. Definitions
2.2. Smart Grid Technologies
2.3. Characteristics of Smart Grids
- Integrable Distributed Resources (DER) that include sustainable energy sources.
- Should perform dynamic optimization of grid operation continuously.
- Should have digitized information and control technologies to enhance the reliability and efficiency of the electric network.
- Should have Demand Side Response (DSR) programs and demand-side resources.
- Integrable smart appliances.
- Are fortified against cyber threats.
- Should have advanced storage devices and peak-shaving technologies, including hybrid and plug-in electric vehicles.
2.4. Benefits of Smart Grid Transformation
- Improve the reliability and quality of the power grid.
- Optimize the operation of existed assets to avert the future expansion of backup plants.
- Enhance the overall system efficiency.
- Improve system resiliency.
- Facilitate the incorporation of Distributed Resources.
- Enable predictive maintenance and self-healing capacities.
- Lower greenhouse gaseous emissions.
- Increase consumers’ assortments.
- Increase the opportunities to enhance system security.
3. Smart Grid Predominant Components
3.1. Distributed Generation
3.2. Reliability Assessment of DGs in Modern Grids
- The availability of the DG: Distributed generation units are exposed to failures that might restrict their functionality. Therefore, reliability models should consider the availability of DGs under different contingency scenarios. Such models require probabilistic approaches to deal with such a kind of stochasticity.
- The operating mode of DGs (Islanded and grid-connected): In Islanded mode, the lack of appropriated control, protection capabilities, and communication infrastructure limits this kind of operation [126]. However, numerous studies have been proposed to address the reliability assessment of the distribution network to facilitate the transformation toward the intelligently managed grid. Whereas in grid-connected mode, DGs are installed close to the load centers to improve the reliability of the system by partially relieving the centralized units during heavy loading conditions. It is, therefore, necessary to quantify the power exchange between the feeders under the presence of DGs. A summary of the prevailing techniques within the layout of microgrids is highlighted in Table 4 and Table 5.
3.3. Demand Response
3.3.1. Reliability of Demand Response
- Reliability-based (Incentive-based) Programs: in this program, a set of demand curtailment signals are sent to the participants in the form of voluntary requests or mandatory commands.
- Rate-based Programs: the prices are set beforehand over a period. Consumers are obliged to pay higher rates during peaking instants and lower prices during off-peak periods. This program is seen as a Time-of-Use (TOU) structure.
- Demand Reduction Bids: in this program, the participating consumers submit their bids to the demand aggregator or the independent system operator (ISO) offering their available capacity to be curtailed.
3.3.2. Applications of Demand Response
3.4. Energy Storage Technologies
4. Data Management
4.1. Energy Data Management in Smart Grids
4.1.1. Data Collection
4.1.2. Data Preprocessing
4.1.3. Data Integration
4.1.4. Data Storage
4.1.5. Data Mining and Data Analytics
4.1.6. Data Visualization
4.1.7. Online Decision-Making
4.1.8. Data Management Challenges in Smart Grids
4.2. Cyber Security of Smart Grids
5. Pricing Mechanisms in Smart Grids
5.1. Dynamic Pricing Mechanisms
5.2. Time-of-Use
5.3. Real-Time Pricing Method
5.4. Critical Peak Pricing
5.5. Day-Ahead Pricing
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Conventional Grid | Smart Grid |
---|---|
Mechanically operated | Digitized |
Unilateral | Bi-directional |
Centralized Power generation | Distributed Generation |
Radially connected | Dispersed |
Small number of sensors | Many |
Less monitoring capabilities | Highly monitored |
Manual control | Automated control |
Less security issues | Vulnerable to security issues |
Slow responsive actions | Fast response |
Category | Description and the Actors in Domain |
---|---|
Customer | Where the electricity is consumed, End-users. The sub-category includes domestics and large consumers, such as commercial and industrial loads. Actors can generate, manage, and store. |
Market | Where the assets are exchanged. The operator and participants are the actors in electricity markets. |
Service Provider | An organization that provides services pertaining to the establishment and secure operation of smart grids as per the requirements of the consumers and utilities. |
Operations | The proper operation of the power system is monitored—the managers of the flow of electricity. |
Bulk Generation | Where the delivery of bulky electricity to consumers starts, actors are the generators of electricity in bulk quantities; energy can also be stored for later distribution. |
Transmission | Where the bulky power is transferred from generation centers to distribution. Actors are the carriers of electricity and may also generate and store electricity. |
Distribution | Where Distributed Generation, Distributed Storage, transmission, and consumers’ interconnect. Actors are the entities that distribute electricity to and from customers. |
Technique | Ref. | Year | Author (s) | Objective |
---|---|---|---|---|
AVR | [52] | 2014 | Ting-Chia et al. |
|
[53] | 2010 | Morris et al. |
| |
GIS | [54] | 2012 | Schneider Electric |
|
[55] | 2009 | Esri Inc |
| |
OMS | [56] | 2013 | John Dirkman |
|
[45] | 2011 | Eduardo et al. |
| |
AGC | [53] | 2014 | Siddaharth et al. |
|
[57] | 2012 | Ali et al. |
| |
AMI | [45] | 2011 | Eduardo et al. |
|
EMS | [58] | 2012 | Rahman et al. |
|
Ref. | Technique | DG Type | Generation Model | Load Model |
---|---|---|---|---|
[128,129] | Analytical | Dispatchable | - | - |
[130,131,132] | Monte Carlo Simulation | Dispatchable Non-Dispatchable | 3 states Markov Models | Averaging |
[133] | Monte Carlo Simulation | Dispatchable Non-Dispatchable | Probabilistic approach | Probabilistic approach |
[133,134,135] | Monte Carlo Simulation | Dispatchable Non-Dispatchable | Hourly profile | Hourly profile |
[136] | Analytical | Dispatchable Non-Dispatchable | Levels of a typical day | Levels of a typical day |
[132,137,138] | Analytical | Dispatchable Non-Dispatchable | Probabilistic approach | Probabilistic approach |
[139,140,141,142] | Analytical | Dispatchable Non-Dispatchable | Segment of a year | Segment of a year |
Ref. | Technique | System Constraints | Power Flow | Transferred Capacity |
---|---|---|---|---|
[143,144] | Analytical | Voltage and load | Yes | Computed |
[145] | Monte Carlo Simulation | Loading | No | Computed |
[146] | Analytical + Monte Carlo Simulation | Loading | No | Computed |
Ref. | Technique | Operating Mode | DR Program | DR Criteria | ICT Impact |
---|---|---|---|---|---|
[161] | Analytical | Grid Connected | Incentivized | Interruption cost minimization | No |
[113] | SMCS | Grid Connected | Incentivized | Shifting less critical loads | No |
[158,162] | Analytical SMCS | Grid Connected | Incentivized | Minimizing interruption cost/payback incentives | Yes |
[159] | SMCS | Off-grid | TOU | Conflictive objectives to maximize the profits of the supplier and to minimize the payments of consumers | Yes |
[160] | SMCS | Off-grid | Incentivized | Interruption cost minimization | Yes |
Energy S. Technology | Energy Density | Power Density | Discharge Time | Lifetime | Capital Cost | Technological Maturity | |
---|---|---|---|---|---|---|---|
Wh/kg | W/kg | (Year) | $/KW | $/KWh | |||
Mechanical Energy Storage | |||||||
PHES | 0.5–1.5 | - | 1–24 h+ | 40–60 | 600–2000 | 5–100 | Matured |
CAES | 30–60 | (0.5–2.0) | 1–24 h+ | 20–40 | 400–800 | 2–50 | Developed |
Flywheel | 10–30 | 400–1500 | Millisec–15 min | 15 | 50–300 | 500–1000 | Commercial |
Electrochemical Energy Storage | |||||||
Lead Acid Battery | 30–50 | 75–300 | Sec–hrs | 5–15 | 200–300 | 120–150 | Commercial |
NiCd Battery | 50–75 | 150–300 | Sec–hrs | 10–20 | 500–1500 | 800–1500 | Commercial |
Sodium Sulfur (NaS)Battery | 150–240 | 150–230 | Sec–hrs | 10–15 | 1000–3000 | 300–500 | Commercial |
Lithium Battery (Li-ion) | 75–250 | 150–315 | Mins–hrs | 5–15 | 1200–4000 | 300–1300 | Demonstration |
VRFB | 10–30 | -- | Sec–10 h | 5–10 | 600–1500 | 50–1000 | Demonstration |
Electrical Energy Storage | |||||||
SuperCapacitor | 2.5–15 | 500–1300 | Millisec–60 Min | 20+ | 100–300 | 300–10,000 | Developed |
SMES | 0.5–5 | 500–2000 | Millisecs–sec | 20+ | 200–300 | 1000–10,000 | Demonstration |
Chemical Energy Storage | |||||||
Hydrogen Fuel Cells | 800–10,000 | 500+ | Sec–24 h+ | 5–15 | – | 6000–20,000 | Developing |
SNG | 10,000 | (0.2–2) | 1–24 h+ | 10–30 | - | - | Developing |
Thermal Energy Storage | |||||||
CSP | −43.05 | - | Mins–hrs | 30 | - | 3500–7000 | Developing |
Energy S. Technology | Power Rating | Storage Duration | Self-Discharge per Day | Cycle Life (cycles) | Round trip Efficiency (%) | Response Time | Class |
---|---|---|---|---|---|---|---|
Mechanical Energy Storage | |||||||
PHES | 100–5000 MW | Hrs–Mons | Very small | – | 65–87% | L–2 min | Long term |
CAES | 5–300 MW | Hrs–Mons | Small | - | 50–89% | 1–2 min | Long term |
Flywheel | 0–250 kW | Sec–Mins | 100% | - | 85–95% | 1–2 min | Short term |
Electrochemical Energy Storage | |||||||
Lead Acid Battery | 0–20 MW | Mins–days | 0.1–0.3% | 500–1000 | 75–80% | Seconds | Long term |
NiCd Battery | 0–40 MW | Mins–days | 0.2–0.6% | 2000–2500 | 85–90% | Seconds | Long term |
Sodium Sulfur (NaS) battery | 50 kW–8 MW | Sec–Hrs | 20% | 2500 | 80–90% | Seconds | Short term |
Lithium Battery (Li-ion) | 0–100 kW | Mins–days | 0.1–0.3% | 1000–10,000+ | 85–90% | Seconds | Long term |
VRFB | 30 kW–3 MW | Hrs–months | Small | 12,000+ | 85–90% | Seconds | Long term |
Electrical Energy Storage Systems | |||||||
Double Layer Capacitor/super Capacitor | 0–300 kW | Sec–hrs | 20–40% | 100,000+ | 90–95% | Milliseconds | Short term |
SMES | 100 kW–10 MW | Sec–hrs | 10–15% | 100,000+ | 95–98% | Millisecond | Short term |
Chemical Energy Storage Systems | |||||||
Hydrogen Fuel cell | 0–50 MW | Hrs–months | Nearly 0 | 100+ | 20–50% | Sec–Mins | Long term |
Thermal Energy Storage Systems | |||||||
CSP | 10 kW–20 MW | – | 1% | – | <60% | 10 min | Long term |
Type of Attack | Mathematical Model |
---|---|
DoS attacks | |
Reply attacks | |
Deception attacks |
Range of Protection | Domain-Based | Detection System |
---|---|---|
SCADA | Networked | [328,329,330] |
Substations | Host | [331] |
Networked | [332] | |
Integrated | [333,334,335] | |
Wide area monitoring (WAM) | Host | [274,336] |
GPS (PMU) | Host | [337] |
Distribution system | Host | [338] |
AMI | Host | [339,340,341,342] |
Ref. | Year | Aim |
---|---|---|
[356,391] | Jan 2019, May 2013 | A game-theoretic approach has been proposed to design an optimal TOU pricing by designing utility functions to reach a Nash equilibrium. |
[357] | August 2012 | The authors proposed variation inequality models to design an optimal TOU pricing policy under different market structures. The proposed study considered the variations in social welfare under different market schemes. |
[358,359] | May 2018, 2013 | The authors have investigated the impact of the existence of DER on the TOU tariffs. |
[392] | December 2013 | The authors used a stochastic optimization approach and quadratic programming to design an optimal TOU price, considering the uncertainties in demand/Generation. |
Ref. | Year | Purpose |
---|---|---|
[367] | Jan. 2019 | The authors have proposed a distributed online pricing strategy for demand-side programs under uncertainties and restricted communication links. Performing the optimization process aims to allocate the minimum operating cost for the utility considering time variant DRs. The proposed study has succeeded in reducing the gap between the online algorithm and the offline optimization process. |
[368] | Dec. 2010 | The authors used a least-square support vector machine to compute short-run tariffs by applying a model predictive control. |
[369] | Jun. 2012 | An optimal load management strategy for residential consumers that uses the communication capability of a typical smart grid was proposed in Reference [369]. The main objective was to allocate the optimal relationship between the spot price and users’ electrical appliances, including electric vehicles in a typical smart building. |
[370] | May 2018 | Markov’s decision-based multi-stage optimization algorithm has been proposed in Reference [370] to maximize the social welfare under the RTP pricing scheme. The optimization process has been divided into sub-problems; the former optimizes the problem from the consumers’ perspectives, while the latter is dedicated to the energy supplier that uses a dual-sub gradient convex optimization. |
[394] | Jan. 2019 | The authors have presented a real-time Energy Management System (EMS) that is suitable for the rooftop PV system integrated with battery storage. The EMS is grid-connected, where the price signal controls the flow of power within the system. The proposed study aims to maximize the revenue over a given cycle using Lagrange multiplier-based optimization algorithm. |
Ref. | Year | Purpose |
---|---|---|
[374] | Jun. 2018 | The authors have combined pricing framework (RTP and CPP) to allocate the optimal operation cycle for a smart home appliance based on a priority list. The proposed study has used an enhanced differential evolution (EDE) and teacher learning-based optimization (TLBO) to attain the maximum satisfaction of consumers. |
[375] | Jan. 2019 | A bi-level framework that aims to maximize the profits of a smart distribution company with electric vehicle parking lots has been proposed. The objective is to minimize the cost of energy purchased from the wholesale market for the leader (e.g., distribution company) and to maximize the profits of parking lots owner (e.g., follower) under Critical Pricing Policy. |
[376] | Dec. 2018 | A mixed-integer linear programming model to allocate the optimal size and optimal planning scheme of Onsite Generation System (OGS) under a Critical Pricing structure has been proposed by Reference [376]. The results have indicated a significant reduction in electricity cost if the OGS is appropriately sized and operated. |
[370] | May 2018 | The authors have proposed a Markov decision-based multi-stage optimization algorithm to maximize the social welfare under the RTP pricing scheme. The optimization process has been divided into sub-problems; the former optimizes the problem from the consumers’ perspectives, while the latter is dedicated to the energy supplier that uses a dual-sub gradient convex optimization. |
[377] | Jun. 2018 | A security-constrained program to schedule the supply–demand by designing an optimal pricing scheme has been proposed. The proposed study aims to find the best pricing modality for different demand-side programs (i.e., TOU, CPP, and RTP) that improves efficiency while guaranteeing the environmental restrictions. A multi-objective optimization approach has been used to minimize the ISO operational cost and the greenhouse emissions resulted from the generating units. |
Ref. | Purpose | |
---|---|---|
[380] | Jan. 2019 | The authors have used a bi-objective optimization model to achieve a minimum electricity cost and to reduce the aggregated peak demands for residential Electric Vehicles (EVs) under a day ahead pricing structure. |
[381] | Jan. 2019 | The authors have discussed the feasibility of trading electrical power as a commodity in a day-ahead electricity market instead of trading energy. The objective of this study is to overcome the coarse discretization that is commonly seen in wholesale energy markets. |
[382] | Jan. 2019 | The authors have proposed an optimal hourly configuration and day head-pricing scheme in a smart distribution system considering the operation of protective devices. The authors have used a metaheuristic-based optimization model to minimize the purchasing power from the wholesale market and distributed resources owners, cost of power loss, cost of switching actions, and the cost of implementing the demand response Programs. |
[383] | Jan. 2019 | A game-theoretic multi-stage optimization model to simultaneously determine the dynamic pricing policy for the Independent System Operator and EV parking lots has been proposed. The study aims to minimize the electricity bills of EV owners while ensuring the profitability of the ISO in a day-ahead pricing framework. |
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Alotaibi, I.; Abido, M.A.; Khalid, M.; Savkin, A.V. A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources. Energies 2020, 13, 6269. https://doi.org/10.3390/en13236269
Alotaibi I, Abido MA, Khalid M, Savkin AV. A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources. Energies. 2020; 13(23):6269. https://doi.org/10.3390/en13236269
Chicago/Turabian StyleAlotaibi, Ibrahim, Mohammed A. Abido, Muhammad Khalid, and Andrey V. Savkin. 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources" Energies 13, no. 23: 6269. https://doi.org/10.3390/en13236269
APA StyleAlotaibi, I., Abido, M. A., Khalid, M., & Savkin, A. V. (2020). A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources. Energies, 13(23), 6269. https://doi.org/10.3390/en13236269