Sustainability

Research

12 pages, 2173 KiB

Open AccessArticle

A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage

by Yu Huang, Shuqin Li, Xinyue Liu, Yan Zhang, Li Sun and Kai Yang

Sustainability 2019, 11(24), 7097; https://doi.org/10.3390/su11247097 - 11 Dec 2019

Cited by 4 | Viewed by 2311

Optimal phasor measurement units (PMU) placement was developed to determine the number and locations of PMUs on the premise of full observability of the whole network. In order to enhance reliability under contingencies, redundancy should also be considered beside the number of PMUs [...] Read more.

Optimal phasor measurement units (PMU) placement was developed to determine the number and locations of PMUs on the premise of full observability of the whole network. In order to enhance reliability under contingencies, redundancy should also be considered beside the number of PMUs in optimal phasor measurement units placement problem. Thus, in this paper, a multi-objective model was established to consider the two conflicting components simultaneously, solved by ε-constraint method and the fuzzy satisfying approach. The redundancy here was formulated as average possibility of observability including random component outages, and full possibility formula was applied to calculate the average possibility of observability in the case of single line outage. Finally, the model was employed to the IEEE-57 bus system, and the results verified that the developed model could provide a placement scheme with higher reliability. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Data Analytics, Modeling, Control and Optimisation of Sustainable Energy Systems)

► Show Figures

Figure 1

21 pages, 1403 KiB

Open AccessArticle

An Improved, Negatively Correlated Search for Solving the Unit Commitment Problem’s Integration with Electric Vehicles

by Qun Niu, Kecheng Jiang and Zhile Yang

Sustainability 2019, 11(24), 6945; https://doi.org/10.3390/su11246945 - 5 Dec 2019

Cited by 5 | Viewed by 2474

Abstract

With the rapid development of plug-in electric vehicles (PEVs), the charging of a number of PEVs has already brought huge impact and burden to the power grid, particularly at the medium and low voltage distribution networks. This presents a big challenge for further [...] Read more.

With the rapid development of plug-in electric vehicles (PEVs), the charging of a number of PEVs has already brought huge impact and burden to the power grid, particularly at the medium and low voltage distribution networks. This presents a big challenge for further mass roll-out of electric vehicles. To assess the impact of charging of substantial number of electric vehicles on the grid, a model of 30000 PEVs integrated with unit commitment (UCEV) was investigated in this study. The unit commitment was a large-scale, mixed-integer, nonlinear, NP-Hard (non-deterministic polynomial) optimization problem, while the integration of PEVs further increased the complexity of the model. In this paper, a global best inspired negatively correlated search (GBNCS) method which extends the evolutionary logic of negatively correlated search is proposed to tackle the UCEV problem. In the proposed algorithm, a rounding transfer function in GBNCS, is deployed to convert real-valued variables into binary ones; further, the global best information is combined in the population to improve the efficiency of the algorithm. Numerical results confirmed that the proposed GBNCS can achieve good performance in both a basic IEEE 10 unit commitment problem and the UCEV problem. It was also shown that, among four charging modes, the off-peak charging mode and EPRI (Electric Power Research Institute) charging mode are more economical in PEV charging. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Data Analytics, Modeling, Control and Optimisation of Sustainable Energy Systems)

► Show Figures

Figure 1

16 pages, 4715 KiB

Open AccessArticle

Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems

by Kamil Neyfel Çerçi and Mehmet Daş

Sustainability 2019, 11(18), 5127; https://doi.org/10.3390/su11185127 - 19 Sep 2019

Cited by 29 | Viewed by 4651

Abstract

As a sustainable energy source, solar energy is used in many applications. A greenhouse type dryer, which is a food drying system, directly benefits from solar energy. Convective heat transfer coefficient (h_c) is an important parameter in food drying systems, [...] Read more.

As a sustainable energy source, solar energy is used in many applications. A greenhouse type dryer, which is a food drying system, directly benefits from solar energy. Convective heat transfer coefficient (h_c) is an important parameter in food drying systems, in terms of system design and performance. Many parameters and equations are used to determine h_c. However, as it is difficult to manually process and analyze large amounts of data and different formulations, machine learning algorithms are preferred. In this study, natural and forced convective solar greenhouse type dryers were designed. In a solar greenhouse type dryer, grape is dried in natural (GDNC) and forced convection (GDFC). For convective heat transfer coefficient (h_c), predictive models were created using a multilayer perceptron (MLP)—which has many uses in drying applications, as mentioned in the literature—and decision tree (DT), which has not been used before in food drying applications. The machine learning algorithms and results of the estimated models are compared in this study. Error analyses were performed to determine the accuracy rates of the obtained models. As a result, the h_c value of the dried grape product in a natural convective solar greenhouse type dryer was 11.3% higher than that of the forced type. The DT algorithm was found to be a more successful model than the MLP algorithm in estimating hc values in HDFC according to Root Mean Square Error. (RMSE = 0.0903). On the contrary, the MLP algorithm was more successful than the DT algorithm in estimating h_c values in GDNC (RMSE = 0.0815). Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Data Analytics, Modeling, Control and Optimisation of Sustainable Energy Systems)

► Show Figures

Graphical abstract

25 pages, 5546 KiB

Open AccessArticle

Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm

by Hongxia Zhu, Gang Zhao, Li Sun and Kwang Y. Lee

Sustainability 2019, 11(18), 5102; https://doi.org/10.3390/su11185102 - 18 Sep 2019

Cited by 13 | Viewed by 2875

Abstract

This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by [...] Read more.

This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Data Analytics, Modeling, Control and Optimisation of Sustainable Energy Systems)

► Show Figures

Figure 1

20 pages, 5509 KiB

Open AccessArticle

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

by Yuxiao Qin, Guodong Zhao, Qingsong Hua, Li Sun and Soumyadeep Nag

Sustainability 2019, 11(12), 3290; https://doi.org/10.3390/su11123290 - 14 Jun 2019

Cited by 14 | Viewed by 3221

Abstract

Nowadays, given the great deal of fossil fuel consumption and associated environmental pollution, solid oxide fuel cells (SOFCs) have shown their great merits in terms of high energy conversion efficiency and low emissions as a stationary power source. To ensure power quality and [...] Read more.

Nowadays, given the great deal of fossil fuel consumption and associated environmental pollution, solid oxide fuel cells (SOFCs) have shown their great merits in terms of high energy conversion efficiency and low emissions as a stationary power source. To ensure power quality and efficiency, both the output voltage and fuel utilization of an SOFC should be tightly controlled. However, these two control objectives usually conflict with each other, making the controller design of an SOFC quite challenging and sophisticated. To this end, a multi-objective genetic algorithm (MOGA) was employed to tune the proportional–integral–derivative (PID) controller parameters through the following steps: (1) Identifying the SOFC system through a least squares method; (2) designing the control based on a relative gain array (RGA) analysis; and (3) applying the MOGA to a simulation to search for a set of optimal solutions. By comparing the control performance of the Pareto solutions, satisfactory control parameters were determined. The simulation results demonstrated that the proposed method could reduce the impact of disturbances and regulate output voltage and fuel utilization simultaneously (with strong robustness). Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Data Analytics, Modeling, Control and Optimisation of Sustainable Energy Systems)

► Show Figures

Figure 1

14 pages, 860 KiB

Open AccessEditor’s ChoiceArticle

Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

by Pascal A. Schirmer and Iosif Mporas

Sustainability 2019, 11(11), 3222; https://doi.org/10.3390/su11113222 - 11 Jun 2019

Cited by 50 | Viewed by 4947

Abstract

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated [...] Read more.

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Data Analytics, Modeling, Control and Optimisation of Sustainable Energy Systems)

► Show Figures

Figure 1

13 pages, 2667 KiB

Open AccessArticle

An Analysis of Disparities and Driving Factors of Carbon Emissions in the Yangtze River Economic Belt

by Decai Tang, Yan Zhang and Brandon J. Bethel

Sustainability 2019, 11(8), 2362; https://doi.org/10.3390/su11082362 - 19 Apr 2019

Cited by 27 | Viewed by 3504

Abstract

As one of the “three major strategies” for China’s regional development, the Yangtze River Economic Belt (YREB) is under severe pressure to reduce carbon dioxide emissions, this paper analyzes the spatiotemporal disparities, and driving factors of carbon emissions based on energy consumption and [...] Read more.

As one of the “three major strategies” for China’s regional development, the Yangtze River Economic Belt (YREB) is under severe pressure to reduce carbon dioxide emissions, this paper analyzes the spatiotemporal disparities, and driving factors of carbon emissions based on energy consumption and related economic development data in the YREB over the 2005–2016 11-year period. Using the Stochastic Impacts Regression on Population, Affluence and Technology (STIRPAT) model, we empirically test the factors affecting YREB carbon emissions and key drivers in various provinces and municipalities. The main findings are as follows. First, per capita GDP, both industrial structure and energy intensity have positive effects on increasing carbon emissions. Second, per capita GDP and energy intensity have the largest impact on the increase of carbon emissions, and the urbanization rate has the largest inhibitory effect on carbon emissions. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Data Analytics, Modeling, Control and Optimisation of Sustainable Energy Systems)

► Show Figures

Figure 1

17 pages, 1904 KiB

Open AccessArticle

A Green Supply-Chain Decision Model for Energy-Saving Products That Accounts for Government Subsidies

by Jian Xue, Ruifeng Gong, Laijun Zhao, Xiaoqing Ji and Yan Xu

Sustainability 2019, 11(8), 2209; https://doi.org/10.3390/su11082209 - 12 Apr 2019

Cited by 53 | Viewed by 4464

Abstract

Government subsidies are a common policy adopted to promote energy conservation and emission reduction. The decision-making that occurs within the green supply chain for energy-saving products under government subsidies is an area of great academic interest and game theory is becoming a popular [...] Read more.

Government subsidies are a common policy adopted to promote energy conservation and emission reduction. The decision-making that occurs within the green supply chain for energy-saving products under government subsidies is an area of great academic interest and game theory is becoming a popular tool in such research. In this paper, we examined centralized and decentralized decision-making models for the green supply chain and a coordinated decision-making model for revenue-sharing contracts based on game theory. We studied the effects of government subsidies on retail prices, energy conservation levels, market demand, supply chain profits, and social welfare for energy-saving products. We then compared the effectiveness of the three models using a numerical example. Our results revealed the range of contract parameters for which manufacturer and retailer profits increase. Our results show that government subsidies can significantly improve social welfare and promote the improvement of energy-saving products. Centralized decision-making generates higher profits than decentralized decisions and government subsidies were positively correlated with the level of energy conservation, product prices, and market demand. Revenue sharing contract coordination decisions can coordinate the supply chain and achieve the same effect as centralized decisions. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Data Analytics, Modeling, Control and Optimisation of Sustainable Energy Systems)

► Show Figures