E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Topical Collection "Power System and Sustainability"

A topical collection in Sustainability (ISSN 2071-1050). This collection belongs to the section "Energy Sustainability".

Editors

Guest Editor
Dr. Gaetano Zizzo

Department of Energy, Information Engineering and Mathematical Models – University of Palermo, Palermo, Italy
Website | E-Mail
Interests: Power Systems design; dynamics and stability; Smart Grids; Demand Response and Transactive Energy; Electric Energy Storage Systems (EESS); Impacts on power systems of renewable energy sources; Electricity Markets
Guest Editor
Prof. Salvatore Favuzza

Department of Energy, Information Engineering and Mathematical Models – University of Palermo, Italy
Website | E-Mail
Interests: power system design; smart grids; smart cities; photovoltaics; electric energy storage systems; ICT for smart grids

Topical Collection Information

Dear Colleagues,

Decarbonization, energy efficiency improvements, and grid integration of distributed generation and storage systems are topical issues for power system research in the new century.

Power systems are required to become more and more smart, green, and sustainable, managed by intelligent devices, allowing also the participation of end-users in the energy share.

New regulations and support policies are driving the development of new strategies and devices for making this epochal transformation real.

In this context, we encourage all researchers from relevant domains to submit papers to this Special Issue on “Power Systems and Sustainability”. Contributions on the following themes, but not limited to them, are welcomed:

  • Insular Power Systems: Design, operations, planning, economics, efficiency improvement;
  • Rural Power Systems: Design, operations, planning, economics, efficiency improvement;
  • Life Cycle Assessment of new high efficiency devices and components for power grids;
  • Impact of BAC and TBM systems on near zero energy buildings
  • Impact of battery storage systems on the generation and distribution efficiency of a microgrid
  • ICT for smart grids
  • Demand Side Management and Demand Response
  • Multi carrier hubs
  • Environmental impact of modern power systems
  • Energy storage for mitigating the variability of renewable electricity sources
  • Electricity from renewable sources
  • Electrical wastes treatment
  • Support policies for battery storage systems

Dr. Gaetano Zizzo
Prof. Salvatore Favuzza
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Insular Power Systems
  • Rural Power Systems
  • Smart Grids Smart Cities
  • Renewable Energy
  • Electric Energy Storage Systems
  • ICT for Smart Grids
  • Waste treatment
  • Support Policies
  • NZEB
  • BAC and TBM systems
  • LCA

Published Papers (30 papers)

2019

Jump to: 2018, 2017

Open AccessArticle Control Strategy of a Hybrid Energy Storage System to Smooth Photovoltaic Power Fluctuations Considering Photovoltaic Output Power Curtailment
Sustainability 2019, 11(5), 1324; https://doi.org/10.3390/su11051324
Received: 16 December 2018 / Revised: 21 February 2019 / Accepted: 26 February 2019 / Published: 3 March 2019
PDF Full-text (3576 KB) | HTML Full-text | XML Full-text
Abstract
The power fluctuations of grid-connected photovoltaic (PV) systems have negative impacts on the power quality and stability of the utility grid. In this study, the combinations of a battery/supercapacitor hybrid energy storage system (HESS) and the PV power curtailment are used to smooth [...] Read more.
The power fluctuations of grid-connected photovoltaic (PV) systems have negative impacts on the power quality and stability of the utility grid. In this study, the combinations of a battery/supercapacitor hybrid energy storage system (HESS) and the PV power curtailment are used to smooth PV power fluctuations. A PV power curtailment algorithm is developed to limit PV power when power fluctuation exceeds the power capacity of the HESS. A multi-objective optimization model is established to dispatch the HESS power, considering energy losses and the state of charge (SOC) of the supercapacitor. To prevent the SOCs of the HESS from approaching their lower limits, a SOC correction strategy is proposed to correct the SOCs of the HESS. Moreover, this paper also investigates the performances (such as the smoothing effects, losses and lifetime of energy storage, and system net profits) of two different smoothing strategies, including the method of using the HESS and the proposed strategy. Finally, numerous simulations are carried out based on data obtained from a 750 kWp PV plant. Simulation results indicate that the proposed method is more economical and can effectively smooth power fluctuations compared with the method of using the HESS. Full article
Figures

Graphical abstract

Open AccessArticle A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization
Sustainability 2019, 11(4), 1188; https://doi.org/10.3390/su11041188
Received: 11 January 2019 / Revised: 16 February 2019 / Accepted: 18 February 2019 / Published: 23 February 2019
PDF Full-text (2968 KB) | HTML Full-text | XML Full-text
Abstract
Combating climate change issues resulting from excessive use of fossil fuels comes with huge initial costs, thereby posing difficult challenges for the least developed countries in Sub-Saharan Africa (SSA) to invest in renewable energy alternatives, especially with rapid industrialization. However, designing renewable energy [...] Read more.
Combating climate change issues resulting from excessive use of fossil fuels comes with huge initial costs, thereby posing difficult challenges for the least developed countries in Sub-Saharan Africa (SSA) to invest in renewable energy alternatives, especially with rapid industrialization. However, designing renewable energy systems usually hinges on different economic and environmental criteria. This paper used the Multi-Objective Particle Swarm Optimization (MOPSO) technique to optimally size ten grid-connected hybrid blocks selected amongst Photo-Voltaic (PV) panels, onshore wind turbines, biomass combustion plant using sugarcane bagasse, Battery Energy Storage System (BESS), and Diesel Generation (DG) system as backup power, to reduce the supply deficit in Sierra Leone. Resource assessment using well-known methods was done for PV, wind, and biomass for proposed plant sites in Kabala District in Northern and Kenema District in Southern Sierra Leone. Long term analysis was done for the ten hybrid blocks projected over 20 years whilst ensuring the following objectives: minimizing the Deficiency of Power Supply Probability (DPSP), Diesel Energy Fraction (DEF), Life Cycle Costs (LCC), and carbon dioxide (CO 2 ) emissions. Capacity factors of 27.41 % and 31.6 % obtained for PV and wind, respectively, indicate that Kabala district is the most feasible location for PV and wind farm installations. The optimum results obtained are compared across selected blocks for DPSP values of 0–50% to determine the most economical and environmentally friendly alternative that policy makers in Sierra Leone and the region could apply to similar cases. Full article
Figures

Figure 1

Open AccessArticle Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms
Sustainability 2019, 11(3), 665; https://doi.org/10.3390/su11030665
Received: 9 January 2019 / Revised: 15 January 2019 / Accepted: 19 January 2019 / Published: 28 January 2019
PDF Full-text (802 KB) | HTML Full-text | XML Full-text
Abstract
This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based [...] Read more.
This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model. Full article
Figures

Figure 1

Open AccessArticle Evaluation of Building Energy and Daylight Performance of Electrochromic Glazing for Optimal Control in Three Different Climate Zones
Sustainability 2019, 11(1), 287; https://doi.org/10.3390/su11010287
Received: 28 October 2018 / Revised: 18 December 2018 / Accepted: 26 December 2018 / Published: 8 January 2019
PDF Full-text (5800 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this paper is to analyze the control conditions of the transmittance rate, and determine the conditions that are most optimal with respect to building energy and daylight performance in three climate conditions: Riyadh, Saudi Arabia (hot climate); Inchon, South Korea [...] Read more.
The objective of this paper is to analyze the control conditions of the transmittance rate, and determine the conditions that are most optimal with respect to building energy and daylight performance in three climate conditions: Riyadh, Saudi Arabia (hot climate); Inchon, South Korea (hot and cold climate); and Moscow, Russia (cold climate). The analysis was based on the electrochromic glass developed by a research team. Electrochromic glass is a next generation solar control glass that can control the transmittance of the glass itself. Therefore, proper control methods are essential for rational use of this electrochromic glass. To properly control electrochromic glass, daylight performance must be considered, along with building energy (heating, cooling, and lighting). If only building energy is considered, transmittance needs to be lowered during the summer season and increased during the winter season. Controlling electrochromic glass transmittance with such a method would not improve the satisfaction of users and occupants of a building due to the resulting glare. In addition to energy reduction, the basic function of solar control glass is to prevent glare. Therefore, in this study, we develop the Energy and Daylight Performance Index (EDPI) using, to evaluate the combined building energy and daylight performance and deduce the optimal control method for electrochromic glass. In addition, optimal control conditions for the three different climatic regions were obtained. Limitations of this study were that the scope was restricted to the eastern climate region, and that the building analysis model was limited to one climate region. It is expected that the optimal control method could be used as an initial database in the development of a electrochromic glass control system. Full article
Figures

Figure 1

Open AccessArticle An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data
Sustainability 2019, 11(1), 251; https://doi.org/10.3390/su11010251
Received: 16 December 2018 / Accepted: 31 December 2018 / Published: 7 January 2019
Cited by 1 | PDF Full-text (4921 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, Smart Grids have been developing globally. Since smart meters only acquire low-frequency data, non-intrusive load monitoring technology using the signature extracted from high-frequency data needs an additional measurement device to be installed, so it is not suitable for promotion to [...] Read more.
In recent years, Smart Grids have been developing globally. Since smart meters only acquire low-frequency data, non-intrusive load monitoring technology using the signature extracted from high-frequency data needs an additional measurement device to be installed, so it is not suitable for promotion to the smart grid environment. However, methods using low-frequency features are poorly-suited when several appliances are switched on at the same time, or devices with similar power values are used. In response to these problems, this paper proposes a load disaggregation method based on the power consumption patterns of appliances, combining an improved mathematical optimization model and optimized bird swarm algorithm (OBSA) for load disaggregation. Experiments show that the method can effectively identify the operating states of appliances, and deal with situations in which multiple instruments have similar power characteristics or are simultaneously switching. The performance comparison proves that the improved model is more efficient than the traditional active and reactive power (PQ) optimization model in load disaggregation performance and computation time, and also verifies the robustness of the proposed method and the convergence of OBSA. As an inexpensive method without extra measurement hardware installed, the process is suitable for large-scale applications in smart grids. Full article
Figures

Figure 1

2018

Jump to: 2019, 2017

Open AccessArticle A Sustainable Power Plant Control Strategy Based on Fuzzy Extended State Observer and Predictive Control
Sustainability 2018, 10(12), 4824; https://doi.org/10.3390/su10124824
Received: 8 November 2018 / Revised: 6 December 2018 / Accepted: 13 December 2018 / Published: 18 December 2018
PDF Full-text (3623 KB) | HTML Full-text | XML Full-text
Abstract
The control of an ultra-supercritical (USC) boiler–turbine power plant is critical in maintaining the safety of the sustainable power grid. However, it is challenging due to the internal nonlinearity, hard manipulation constraints, and widespread uncertainties. To this end, a fuzzy extended state observer [...] Read more.
The control of an ultra-supercritical (USC) boiler–turbine power plant is critical in maintaining the safety of the sustainable power grid. However, it is challenging due to the internal nonlinearity, hard manipulation constraints, and widespread uncertainties. To this end, a fuzzy extended state observer (FESO)-based stable fuzzy predictive control (SFPC) approach is developed in this paper. First, the control difficulties of the USC boiler–turbine unit are analyzed. Then, based on a Takagi–Sugeno (T–S) fuzzy model, a new FESO is developed for nonlinear systems to achieve a more precise observation performance. The gain of FESO is determined by solving a series of linear matrix inequalities, while guaranteeing the stability of FESO. Then, by combining the proposed FESO with the SFPC, an integrated FESO–SFPC algorithm is devised. The disturbance rejection ability of the FESO–SFPC algorithm is analyzed theoretically. Simulation results on a 1000 MW USC boiler–turbine power plant model further validate the effectiveness of the proposed method. Full article
Figures

Figure 1

Open AccessArticle The Active Power Losses in the Road Lighting Installation with Dimmable LED Luminaires
Sustainability 2018, 10(12), 4742; https://doi.org/10.3390/su10124742
Received: 9 November 2018 / Revised: 3 December 2018 / Accepted: 9 December 2018 / Published: 12 December 2018
PDF Full-text (3243 KB) | HTML Full-text | XML Full-text
Abstract
In accordance with the requirements of PN EN 13201-5 standard for road lighting installation, energy performance indicators should be descripted. In order to calculate energy performance indicators, it is necessary to know the active power of the road lighting system. The above standard [...] Read more.
In accordance with the requirements of PN EN 13201-5 standard for road lighting installation, energy performance indicators should be descripted. In order to calculate energy performance indicators, it is necessary to know the active power of the road lighting system. The above standard does not specify whether active power losses should be taken into account in calculations. The main purpose of the article is to estimate the active power losses in the road lighting installation. The article presents methods for calculating active power losses, taking into account losses in all main elements of the installation. The obtained calculation results show the relationship between active power losses and the power of luminaires, their number and spacing between poles. Calculations of active power losses were made for single-phase and three-phase installations. The active power losses in a three-phase system do not exceed 1.5% and in a single-phase installation they may be greater than 7%. Therefore, in order to obtain exact values of energy performance indicators (and also predict electricity consumption), active power losses should be taken into account in calculations. In addition, a comparative analysis of the effect of luminaires dimming and active power losses on annual CO2 emissions was made. Not taking into account the active power losses in the calculation of the lighting installation’s power, for single-phase installations in particular, understates the calculated value of CO2 emissions by more than 6%. Full article
Figures

Figure 1

Open AccessArticle Robust Day-Ahead Scheduling of Electricity and Natural Gas Systems via a Risk-Averse Adjustable Uncertainty Set Approach
Sustainability 2018, 10(11), 3848; https://doi.org/10.3390/su10113848
Received: 8 October 2018 / Revised: 19 October 2018 / Accepted: 20 October 2018 / Published: 24 October 2018
PDF Full-text (494 KB) | HTML Full-text | XML Full-text
Abstract
The requirement for energy sustainability drives the development of renewable energy technologies and gas-fired power generation. The increasing installation of gas-fired units significantly intensifies the interdependency between the electricity system and natural gas system. The joint scheduling of electricity and natural gas systems [...] Read more.
The requirement for energy sustainability drives the development of renewable energy technologies and gas-fired power generation. The increasing installation of gas-fired units significantly intensifies the interdependency between the electricity system and natural gas system. The joint scheduling of electricity and natural gas systems has become an attractive option for improving energy efficiency. This paper proposes a robust day-ahead scheduling model for electricity and natural gas system, which minimizes the total cost including fuel cost, spinning reserve cost and cost of operational risk while ensuring the feasibility for all scenarios within the uncertainty set. Different from the conventional robust optimization with predefined uncertainty set, a new approach with risk-averse adjustable uncertainty set is proposed in this paper to mitigate the conservatism. Furthermore, the Wasserstein–Moment metric is applied to construct ambiguity sets for computing operational risk. The proposed scheduling model is solved by the column-and-constraint generation method. The effectiveness of the proposed approach is tested on a 6-bus test system and a 118-bus system. Full article
Figures

Figure 1

Open AccessArticle Resource Assessment and Techno-Economic Analysis of a Grid-Connected Solar PV-Wind Hybrid System for Different Locations in Saudi Arabia
Sustainability 2018, 10(10), 3690; https://doi.org/10.3390/su10103690
Received: 4 August 2018 / Revised: 10 October 2018 / Accepted: 10 October 2018 / Published: 15 October 2018
Cited by 2 | PDF Full-text (9489 KB) | HTML Full-text | XML Full-text
Abstract
The economic growth and demographic progression in Saudi Arabia increased spending on the development of conventional power plants to meet the national energy demand. The conventional generation and continued use of fossil fuels as the main source of electricity will raise the operational [...] Read more.
The economic growth and demographic progression in Saudi Arabia increased spending on the development of conventional power plants to meet the national energy demand. The conventional generation and continued use of fossil fuels as the main source of electricity will raise the operational environmental impact of electricity generation. Therefore, using different renewable energy sources might be a solution to this issue. In this study, a grid-connected solar PV-wind hybrid energy system has been designed considering an average community load demand of 15,000 kWh/day and a peak load of 2395 kW. HOMER software is used to assess the potential of renewable energy resources and perform the technical and economic analyses of the grid-connected hybrid system. The meteorological data was collected from the Renewable Resources Atlas developed by the King Abdullah City of Atomic and Renewable Energy (KACARE). Four different cities in the Kingdom of Saudi Arabia, namely, the cities of Riyadh, Hafar Albatin, Sharurah, and Yanbu were selected to do the analyses. The simulation results show that the proposed system is economically and environmentally feasible at Yanbu city. The system at this city has the lowest net present cost (NPC) and levelized the cost of energy (LCOE), highest total energy that can be sold to the grid, as well as the lowest CO2 emissions due to a highly renewable energy penetration. This grid-connected hybrid system with the proposed configuration is applicable for similar meteorological and environmental conditions in the region, and around the world. Reduction of some greenhouse gasses as well as the reduction of energy costs are main contributors of this research. Full article
Figures

Figure 1

Open AccessArticle Decomposition Analysis in Electricity Sector Output from Carbon Emissions in China
Sustainability 2018, 10(9), 3251; https://doi.org/10.3390/su10093251
Received: 7 August 2018 / Revised: 31 August 2018 / Accepted: 1 September 2018 / Published: 12 September 2018
Cited by 1 | PDF Full-text (2924 KB) | HTML Full-text | XML Full-text
Abstract
Carbon emissions from China’s electricity sector account for about one-seventh of the global carbon dioxide emissions, or half of China’s carbon dioxide emissions. A better understanding of the relationship between CO2 emissions and electric output would help develop and adjust carbon emission [...] Read more.
Carbon emissions from China’s electricity sector account for about one-seventh of the global carbon dioxide emissions, or half of China’s carbon dioxide emissions. A better understanding of the relationship between CO2 emissions and electric output would help develop and adjust carbon emission mitigation strategies for China’s electricity sector. Thus, we applied the electricity elasticity of carbon emissions to a decoupling index that we combined with advanced multilevel Logarithmic Mean Divisia Index tools in order to test the carbon emission response to the electric output and the main drivers. Then, we proposed a comparative decoupling stability analysis method. The results show that the electric output effect played the most significant role in increasing CO2 emissions from China’s electric sector. Also, “relative decoupling” was the main state during the study period (1991–2012). Moreover, the electricity elasticity of CO2 emissions had a better performance regarding stability in the analysis of China’s electricity output. Full article
Figures

Figure 1

Open AccessArticle Effects of Market Reform on Facility Investment in Electric Power Industry: Panel Data Analysis of 27 Countries
Sustainability 2018, 10(9), 3235; https://doi.org/10.3390/su10093235
Received: 26 August 2018 / Revised: 5 September 2018 / Accepted: 5 September 2018 / Published: 10 September 2018
PDF Full-text (798 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we analyzed the effects of electricity market reform on investment in generation facilities. We used the data of 27 OECD member countries and considered ownership structure, horizontal and vertical unbundling, change of transaction method, and government regulation as explanatory variables [...] Read more.
In this study, we analyzed the effects of electricity market reform on investment in generation facilities. We used the data of 27 OECD member countries and considered ownership structure, horizontal and vertical unbundling, change of transaction method, and government regulation as explanatory variables for market reform. We used four regression models, in which we examined the effects of market reform on the capacity of generation facilities, supply reserve ratio, total investment, and base-load share, respectively. For each panel regression model, we performed a Hausman test to identify the model between random effect and fixed effect. Based on the estimation results, we found that electricity market reform has a negative effect on generation facilities in most countries. Both privatization and regulation have negative impacts on the generation facility and base-load share. On the other hand, the level of liberalization of transactions have positive effects on the generation facility, supply reserve ratio, and base-load share. The empirical analysis also showed that horizontal unbundling does not have a meaningful effect on investment, but vertical unbundling contributes to increasing the supply reserve ratio. Full article
Figures

Figure 1

Open AccessArticle Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression
Sustainability 2018, 10(8), 2889; https://doi.org/10.3390/su10082889
Received: 29 June 2018 / Revised: 3 August 2018 / Accepted: 7 August 2018 / Published: 15 August 2018
Cited by 1 | PDF Full-text (2893 KB) | HTML Full-text | XML Full-text
Abstract
Solar irradiation is influenced by many meteorological features, which results in a complex structure meaning its prediction has low efficiency and accuracy. The existing prediction methods are focused on analyzing the correlation between features and irradiation to reduce model complexity but they do [...] Read more.
Solar irradiation is influenced by many meteorological features, which results in a complex structure meaning its prediction has low efficiency and accuracy. The existing prediction methods are focused on analyzing the correlation between features and irradiation to reduce model complexity but they do not account for redundant analysis in feature subset. In order to reduce the information redundancy in the feature set and improve prediction accuracy, a novel feature selection method for short-term irradiation prediction based on Conditional Mutual Information (CMI) and Gaussian Process Regression (GPR) is proposed. Firstly, the CMI values of different features are calculated to evaluate correlation and redundant information between features in the feature subsets. Secondly, GPR with a stable prediction performance and adaptively determined hyper parameters is used as the predictor. The optimal feature subset and the GPR covariance function can be selected using Sequential Forward Selection (SFS). Finally, an optimal predictor is determined by the minimum prediction error and the prediction of solar irradiation is carried out by the determined predictor. The experimental results show that CMI-GPRAEK has the highest prediction accuracy with the optimal feature set has low dimension, which is 4.33% lower in MAPE than the predictor without feature selection, although both of them have an optimal kernel function. The CMI-GPRAEK is less complicated for the predictor and there is less redundancy between features in the model with the dimension of the optimal feature set is only 14. Full article
Figures

Figure 1

Open AccessArticle Electricity as a Cooking Means in Nepal—A Modelling Tool Approach
Sustainability 2018, 10(8), 2841; https://doi.org/10.3390/su10082841
Received: 4 May 2018 / Revised: 2 August 2018 / Accepted: 4 August 2018 / Published: 10 August 2018
Cited by 2 | PDF Full-text (4316 KB) | HTML Full-text | XML Full-text
Abstract
Cooking energy has an important role in energy demand of Nepal. Over the last decade, import of Liquefied Petroleum Gas (LPG) has increased by 3.3 times as an alternate cooking fuel to kerosene and firewood. The growing subsidy burden to endorse modern fuel [...] Read more.
Cooking energy has an important role in energy demand of Nepal. Over the last decade, import of Liquefied Petroleum Gas (LPG) has increased by 3.3 times as an alternate cooking fuel to kerosene and firewood. The growing subsidy burden to endorse modern fuel switching from traditional energy sources and high import of LPG are challenges for sustainability and energy security. This paper analyzes the future residential cooking energy demand and its environmental and economic impacts from 2015 to 2035 using a Long-range Energy Alternative Planning System (LEAP) tool. In 2035, the LPG demand for cooking is projected to be 26.5 million GJ, 16.3 million GJ, 45.2 million GJ and 58.2 million GJ for business as usual (BAU), low growth rate (LGR), medium growth rate (MGR) and high growth rate (HGR) scenarios, respectively. To substitute LPG with electricity in the cooking sector by 2035, an additional 1207 MW, 734 MW, 2055 MW and 2626 MW hydropower installation is required for BAU, LGR, MGR and HGR scenarios, respectively. In the MGR scenario, substituting LPG with electricity could save from $21.8 million (2016) to $70.8 million (2035) each year, which could be used to develop large-scale hydropower projects in the long term. Full article
Figures

Figure 1

Open AccessArticle Restoration of an Active MV Distribution Grid with a Battery ESS: A Real Case Study
Sustainability 2018, 10(6), 2058; https://doi.org/10.3390/su10062058
Received: 26 April 2018 / Revised: 12 June 2018 / Accepted: 15 June 2018 / Published: 17 June 2018
Cited by 1 | PDF Full-text (5119 KB) | HTML Full-text | XML Full-text
Abstract
In order to improve power system operation, Battery Energy Storage Systems (BESSs) have been installed in high voltage/medium voltage stations by Distribution System Operators (DSOs) around the world. Support for restoration of MV distribution networks after a blackout or HV interruption is among [...] Read more.
In order to improve power system operation, Battery Energy Storage Systems (BESSs) have been installed in high voltage/medium voltage stations by Distribution System Operators (DSOs) around the world. Support for restoration of MV distribution networks after a blackout or HV interruption is among the possible new functionalities of BESSs. With the aim to improve quality of service, the present paper investigates whether a BESS, installed in the HV/MV substation, can improve the restoration process indicators of a distribution grid. As a case study, an actual active distribution network of e-distribuzione, the main Italian DSO, has been explored. The existing network is located in central Italy. It supplies two municipalities of approximately 10,000 inhabitants and includes renewable generation plants. Several configurations are considered, based on: the state of the grid at blackout time; the BESS state of charge; and the involvement of Dispersed Generation (DG) in the restoration process. Three restoration plans (RPs) have been defined, involving the BESS alone, or in coordination with DG. A MATLAB®/Simulink® program has been designed to simulate the restoration process in each configuration and restoration plan. The results show that the BESS improves restoration process quality indicators in different simulated configurations, allowing the operation in controlled island mode of parts of distribution grids, during interruptions or blackout conditions. The defined restoration plans set the priority and the sequence of controlled island operations of parts of the grid to ensure a safe and better restoration. In conclusion, the results demonstrate that a BESS can be a valuable element towards an improved restoration procedure. Full article
Figures

Figure 1

Open AccessArticle The Evolution of Renewable Energy Price Policies Based on Improved Bass Model: A System Dynamics (SD) Analysis
Sustainability 2018, 10(6), 1748; https://doi.org/10.3390/su10061748
Received: 5 May 2018 / Revised: 21 May 2018 / Accepted: 25 May 2018 / Published: 26 May 2018
Cited by 1 | PDF Full-text (5957 KB) | HTML Full-text | XML Full-text
Abstract
Many countries in the world have implemented many price support policies to promote the development of renewable energy, and there are evolutionary processes between different policies at different stages of national development. Existing literature has less research on the internal mechanism and alternative [...] Read more.
Many countries in the world have implemented many price support policies to promote the development of renewable energy, and there are evolutionary processes between different policies at different stages of national development. Existing literature has less research on the internal mechanism and alternative process of renewable energy price policies’ evolution process. In view of this, this paper innovatively introduces the classic model of innovation diffusion theory, the Bass model, into the renewable energy price mechanism, and improves it on the basis of the traditional Bass model, and then proposes a system dynamics (SD) simulation based on the improved Bass model to study the evolution process of the renewable energy price policies. This paper mainly studies the evolution process of the policies from feed-in tariff (FIT) to renewable portfolio standard (RPS), and takes China’s wind power industry as an example to simulate the model. The results show that FIT can effectively and quickly evolve to RPS based on the internal influence of the interaction among power generation enterprises and the external influence of government behaviors. All the power generation enterprises will implement RPS, and the amount of green power enterprises eventually grows steadily and slowly. In addition, increasing the decline rate of FIT subsidy and RPS unit fine can effectively promote the evolution of RPS policy, and also improve the amount of green power enterprises and the activity of the tradable green certificates (TGC) trading market. Full article
Figures

Figure 1

Open AccessArticle Impact of Asynchronous Renewable Generation Infeed on Grid Frequency: Analysis Based on Synchrophasor Measurements
Sustainability 2018, 10(5), 1605; https://doi.org/10.3390/su10051605
Received: 16 April 2018 / Revised: 11 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
Cited by 1 | PDF Full-text (560 KB) | HTML Full-text | XML Full-text
Abstract
The increasing power in-feed of Non-Synchronous Renewable Energy Sources (NS-RES) in the grid has raised concerns about the frequency stability. The volatile RES power output and absence of inertia in many types of NS-RES affect the balance between power consumption and production. Therefore, [...] Read more.
The increasing power in-feed of Non-Synchronous Renewable Energy Sources (NS-RES) in the grid has raised concerns about the frequency stability. The volatile RES power output and absence of inertia in many types of NS-RES affect the balance between power consumption and production. Therefore, the dynamics of the power grid frequency become more complex. Extreme grid frequency deviations and fast variations can lead to partitioning and load shedding in the case of under-frequency. In the case of over-frequency, it can lead to overloading, voltage collapse and blackouts. The Rate of Change of Frequency (RoCoF) reflects an aspect of the stability status of the grid and therefore its analysis with regard to Non-Synchronous Instant Penetration (NSIP) is of great importance. In this work, two months of high-resolution frequency synchrophasor measurements during 18 January 2018–18 March 2018 recorded in Austria were analyzed to investigate the impact of NS-RES on the frequency. The correlation of RoCoF with the NSIP in Austria and Germany and with the frequency deviation were examined. It was observed that with a maximum NSIP share up to 74 % of the total power generation in these two countries, there was no critical increase of RoCoF or abnormal frequency deviation in the power grid. Full article
Figures

Figure 1

Open AccessArticle Cross-Subsidies and Government Transfers: Impacts on Electricity Service Quality in Colombia
Sustainability 2018, 10(5), 1599; https://doi.org/10.3390/su10051599
Received: 17 April 2018 / Revised: 10 May 2018 / Accepted: 11 May 2018 / Published: 16 May 2018
PDF Full-text (1372 KB) | HTML Full-text | XML Full-text
Abstract
An affordable and reliable supply of electricity service is essential to encourage sustainable social development in developing countries. Colombia uses cross-subsidies to prompt electricity usage for poor households. This raises the issue of whether charging lower prices to poor households, while boosting their [...] Read more.
An affordable and reliable supply of electricity service is essential to encourage sustainable social development in developing countries. Colombia uses cross-subsidies to prompt electricity usage for poor households. This raises the issue of whether charging lower prices to poor households, while boosting their consumption, induces utilities to lower the quality of service received by them. This paper uses unique databases and examines how underfunded cross-subsidies affect perceived electricity service quality across consumer groups. Results indicate that when facing financial deficits, utilities provide lower perceived service quality to subsidized consumers than to residents paying surcharges. The difference in perceived quality across consumer groups is reduced by an increase in the amount of (external) government transfers. To prompt electricity consumption by the poor, the Colombian government should fund subsidies, strengthen quality regulation, and increase the transparency and reliability of government transfers. Full article
Figures

Figure 1

Open AccessArticle An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks
Sustainability 2018, 10(4), 1280; https://doi.org/10.3390/su10041280
Received: 19 March 2018 / Revised: 17 April 2018 / Accepted: 19 April 2018 / Published: 21 April 2018
Cited by 5 | PDF Full-text (9017 KB) | HTML Full-text | XML Full-text
Abstract
Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction [...] Read more.
Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper. Full article
Figures

Figure 1

Open AccessArticle Public Willingness to Pay for Increasing Photovoltaic Power Generation: The Case of Korea
Sustainability 2018, 10(4), 1196; https://doi.org/10.3390/su10041196
Received: 10 March 2018 / Revised: 10 April 2018 / Accepted: 10 April 2018 / Published: 16 April 2018
Cited by 2 | PDF Full-text (283 KB) | HTML Full-text | XML Full-text
Abstract
Renewable energy receives particular attention in Korea because of concerns about climate change and scarce traditional energy resources. The government plans to enhance photovoltaic (PV) power’s share of total power generation from 0.5% in 2014 to 10.1% in 2029. The present study tries [...] Read more.
Renewable energy receives particular attention in Korea because of concerns about climate change and scarce traditional energy resources. The government plans to enhance photovoltaic (PV) power’s share of total power generation from 0.5% in 2014 to 10.1% in 2029. The present study tries to look into the public willingness to pay (WTP) for increasing PV power generation, applying the contingent valuation approach. A survey of 1000 interviewees was carried out in Korea. The observations of the WTP responses were gathered using a dichotomous choice question and analyzed employing the mixture model. The mean household WTP estimate is obtained as KRW 2183 (USD 1.9) per month, which possesses statistical significance. The total yearly WTP expanded to the population is worth KRW 476.9 billion (USD 423.1 million). These values can provide a useful basis for policy-making and decision-making about the economic feasibility of increasing PV power generation. Full article
Open AccessArticle Optimal Locating of Electric Vehicle Charging Stations by Application of Genetic Algorithm
Sustainability 2018, 10(4), 1076; https://doi.org/10.3390/su10041076
Received: 2 January 2018 / Revised: 20 March 2018 / Accepted: 28 March 2018 / Published: 4 April 2018
Cited by 2 | PDF Full-text (33343 KB) | HTML Full-text | XML Full-text
Abstract
The advent of alternative vehicle technologies such as Electrical Vehicles (EVs) is an efficient effort to reduce the emission of carbon oxides and nitrogen oxides. Ironically, EVs poses concerns related to vehicle recharging and management. Due to the significance of charging station infrastructure, [...] Read more.
The advent of alternative vehicle technologies such as Electrical Vehicles (EVs) is an efficient effort to reduce the emission of carbon oxides and nitrogen oxides. Ironically, EVs poses concerns related to vehicle recharging and management. Due to the significance of charging station infrastructure, electric vehicles’ charging stations deployment is investigated in this work. Its aim is to consider several limitations such as the power of charging station, the average time needed for each recharge, and traveling distance per day. Initially, a mathematical formulation of the problem is framed. Then, this problem is optimized by application of Genetic Algorithm (GA), with the objective to calculate the necessary number of charging stations then finding the best positions to locate them to satisfy the clients demand. Full article
Figures

Figure 1

Open AccessArticle A DSM Test Case Applied on an End-to-End System, from Consumer to Energy Provider
Sustainability 2018, 10(4), 935; https://doi.org/10.3390/su10040935
Received: 1 February 2018 / Revised: 16 March 2018 / Accepted: 18 March 2018 / Published: 23 March 2018
Cited by 3 | PDF Full-text (35564 KB) | HTML Full-text | XML Full-text
Abstract
Current decarbonisation goals have, in recent years, led to a tremendous increase in electricity production generated from intermittent Renewable Energy Sources. Despite their contribution to reducing society’s carbon dioxide (CO2) emissions they have been responsible for numerous challenges that the current [...] Read more.
Current decarbonisation goals have, in recent years, led to a tremendous increase in electricity production generated from intermittent Renewable Energy Sources. Despite their contribution to reducing society’s carbon dioxide (CO2) emissions they have been responsible for numerous challenges that the current electricity grid has to cope with. Flexibility has become a key mechanism to help in mitigating them. Real-time informed consumers can offer the needed flexibility through modifying their behaviour or by engaging with Demand Side Management (DSM) programs. The latter requires the intervention of several actors and levels of communication management which makes this task difficult from an implementation perspective. With this aim we built and tested a small scale system in our lab which represents a real end-to-end system from the consumer to the energy provider. We programmed the system according to the Object Identification System (OBIS) specification to obtain consumers’ consumption through smart meters with high frequency (one minute). This allows remote control of their appliances in order to reduce the total neighbourhood consumption during critical time periods of the day (peak time). These results and the realisation of a realistic end-to-end system open the way to more complex tests and particularly to the possibility of benchmarking them with other lab tests. Full article
Figures

Figure 1

Open AccessArticle A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation
Sustainability 2018, 10(3), 820; https://doi.org/10.3390/su10030820
Received: 24 January 2018 / Revised: 3 March 2018 / Accepted: 13 March 2018 / Published: 15 March 2018
PDF Full-text (3198 KB) | HTML Full-text | XML Full-text
Abstract
A hybrid photovoltaic (PV) forecasting model is proposed for the ultrashort-term prediction of PV output. The model contains two parts: offline modeling and online forecasting. The offline module uses historical monitoring data to establish a weather type classification model and PV output regression [...] Read more.
A hybrid photovoltaic (PV) forecasting model is proposed for the ultrashort-term prediction of PV output. The model contains two parts: offline modeling and online forecasting. The offline module uses historical monitoring data to establish a weather type classification model and PV output regression submodels. The online module uses real-time monitoring data for weather type identification on target days and the forecasting of irradiation intensity and temperature time series. The appropriate regression submodel can be selected based on the subsequent results, and the ultrashort-term real-time forecasting of PV output can be performed over a short time scale. The model incorporates power generation and historical meteorological data from the PV station and is suitable for practical engineering applications. In addition to the irradiation intensity and temperature, other factors related to photovoltaic output are evaluated; however, they are excluded from the model for simplicity and efficiency. The performance of the model is verified by practical modeling analysis. Full article
Figures

Figure 1

Open AccessArticle Optimal Placement and Sizing of PV-STATCOM in Power Systems Using Empirical Data and Adaptive Particle Swarm Optimization
Sustainability 2018, 10(3), 727; https://doi.org/10.3390/su10030727
Received: 12 February 2018 / Revised: 1 March 2018 / Accepted: 4 March 2018 / Published: 7 March 2018
PDF Full-text (5398 KB) | HTML Full-text | XML Full-text
Abstract
Solar energy is a source of free, clean energy which avoids the destructive effects on the environment that have long been caused by power generation. Solar energy technology rivals fossil fuels, and its development has increased recently. Photovoltaic (PV) solar farms can only [...] Read more.
Solar energy is a source of free, clean energy which avoids the destructive effects on the environment that have long been caused by power generation. Solar energy technology rivals fossil fuels, and its development has increased recently. Photovoltaic (PV) solar farms can only produce active power during the day, while at night, they are completely idle. At the same time, though, active power should be supported by reactive power. Reactive power compensation in power systems improves power quality and stability. The use during the night of a PV solar farm inverter as a static synchronous compensator (or PV-STATCOM device) has recently been proposed which can improve system performance and increase the utility of a PV solar farm. In this paper, a method for optimal PV-STATCOM placement and sizing is proposed using empirical data. Considering the objectives of power loss and cost minimization as well as voltage improvement, two sub-problems of placement and sizing, respectively, are solved by a power loss index and adaptive particle swarm optimization (APSO). Test results show that APSO not only performs better in finding optimal solutions but also converges faster compared with bee colony optimization (BCO) and lightening search algorithm (LSA). Installation of a PV solar farm, STATCOM, and PV-STATCOM in a system are each evaluated in terms of efficiency and cost. Full article
Figures

Figure 1

Open AccessArticle A Statistical Tool to Detect and Locate Abnormal Operating Conditions in Photovoltaic Systems
Sustainability 2018, 10(3), 608; https://doi.org/10.3390/su10030608
Received: 30 January 2018 / Revised: 16 February 2018 / Accepted: 21 February 2018 / Published: 27 February 2018
Cited by 1 | PDF Full-text (3604 KB) | HTML Full-text | XML Full-text
Abstract
The paper is focused on the energy performance of the photovoltaic systems constituted by several arrays. The main idea is to compare the statistical distributions of the energy dataset of the arrays. For small-medium-size photovoltaic plant, the environmental conditions affect equally all the [...] Read more.
The paper is focused on the energy performance of the photovoltaic systems constituted by several arrays. The main idea is to compare the statistical distributions of the energy dataset of the arrays. For small-medium-size photovoltaic plant, the environmental conditions affect equally all the arrays, so the comparative procedure is independent from the solar radiation and the cell temperature; therefore, it can also be applied to a photovoltaic plant not equipped by a weather station. If the procedure is iterated and new energy data are added at each new run, the analysis becomes cumulative and allows following the trend of some benchmarks. The methodology is based on an algorithm, which suggests the user, step by step, the suitable statistical tool to use. The first one is the Hartigan’s dip test that is able to discriminate the unimodal distribution from the multimodal one. This stage is very important to decide whether a parametric test can be used or not, because the parametric tests—based on known distributions—are usually more performing than the nonparametric ones. The procedure is effective in detecting and locating abnormal operating conditions, before they become failures. A case study is proposed, based on a real operating photovoltaic plant. Three periods are separately analyzed: one month, six months, and one year. Full article
Figures

Figure 1

Open AccessArticle Comparative Study of Frequency Converters for Doubly Fed Induction Machines
Sustainability 2018, 10(3), 594; https://doi.org/10.3390/su10030594
Received: 21 January 2018 / Revised: 19 February 2018 / Accepted: 22 February 2018 / Published: 26 February 2018
Cited by 1 | PDF Full-text (6551 KB) | HTML Full-text | XML Full-text
Abstract
The efficient utilization of energy sources seems to be one of the most challenging problems for designers and scientists alike. This challenge particularly applies to power electronics, where the increasing value of energy density leads to demands for optimization processes and better exploitation [...] Read more.
The efficient utilization of energy sources seems to be one of the most challenging problems for designers and scientists alike. This challenge particularly applies to power electronics, where the increasing value of energy density leads to demands for optimization processes and better exploitation (and distribution) of available power sources. As a result, the implementation of frequency-controlled systems is more often in the spotlight. The systems with doubly fed induction machines and a frequency converter in the rotor circuit are typical representatives of these demands. In a wide spectrum of power electronic systems, frequency converters are often used that have a constant current, a diode rectifier, and a thyristor inverter. This article provides a novel approach to modeling methodology, and presents a unique comparison of four different frequency converter schemes that are connected to a doubly fed induction machine. This article presents the modeling methodology itself, as well as the results based on an asynchronous generator motor fed by different frequency converters, a spectral analysis of the output voltage of the used frequency converters, and a comparison of the different technologies. Based on the above, this paper recommends the use of a multistage-multilevel frequency converter scheme. Full article
Figures

Figure 1

Open AccessArticle Why the Wind Curtailment of Northwest China Remains High
Sustainability 2018, 10(2), 570; https://doi.org/10.3390/su10020570
Received: 5 January 2018 / Revised: 2 February 2018 / Accepted: 6 February 2018 / Published: 24 February 2018
Cited by 1 | PDF Full-text (1866 KB) | HTML Full-text | XML Full-text
Abstract
The total grid-connected installed capacity of wind power in northwest China has grown from 16,260 MW in 2013 to 43,290 MW in 2016; an increase of 88.7% each year. However, this region has suffered from increasingly serious wind curtailment since 2014, and the [...] Read more.
The total grid-connected installed capacity of wind power in northwest China has grown from 16,260 MW in 2013 to 43,290 MW in 2016; an increase of 88.7% each year. However, this region has suffered from increasingly serious wind curtailment since 2014, and the wind curtailment amount accounts for nearly a half of China’s total. The wind curtailment rate of Gansu Province, Xinjiang Uygur Autonomous Region and Ningxia Hui Autonomous Region in this area has increased and remains high. This paper constructs an analytical model to explore the reasons of the high wind curtailment of these three provinces from the four aspects of the wind power supply capacity, demand, grid transmission capacity, power system flexibility and market mechanism and laws. The results show that the relationship between the wind energy distribution and supply and the local load is incompatible, which is the source causing the high wind curtailment in northwest China. On the one hand, the game between the local government and developers has driven the development of wind power bases. On the other hand, the electricity sector is growing slowly and oversupply of electricity is seen in many areas of China. The wind power grid of northwest China not only faces limit of grid transmission capacity, but also constraint of insufficient flexibility of the electricity system. Presently, China has not set up a market mechanism and subsidy mechanism for the peak load adjustment, thus the thermal power companies lack motivation to voluntarily adjust the peak load. Moreover, the regional segregation and market barriers are also obstacles for the wind power outward transmission. Full article
Figures

Figure 1

Open AccessArticle Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network
Sustainability 2018, 10(2), 318; https://doi.org/10.3390/su10020318
Received: 27 December 2017 / Revised: 21 January 2018 / Accepted: 22 January 2018 / Published: 26 January 2018
Cited by 1 | PDF Full-text (3276 KB) | HTML Full-text | XML Full-text
Abstract
With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This [...] Read more.
With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This paper presents a new architecture for the medium voltage AC/DC hybrid distribution network; where the AC and DC subgrids are looped by normally closed AC soft open point (ACSOP) and DC soft open point (DCSOP); respectively. The proposed AC/DC hybrid distribution systems contain renewable generation (i.e., wind power and photovoltaic (PV) generation); energy storage systems (ESSs); soft open points (SOPs); and both AC and DC flexible demands. An energy management strategy for the hybrid system is presented based on the dynamic optimal power flow (DOPF) method. The main objective of the proposed power scheduling strategy is to minimize the operating cost and reduce the curtailment of renewable generation while meeting operational and technical constraints. The proposed approach is verified in five scenarios. The five scenarios are classified as pure AC system; hybrid AC/DC system; hybrid system with interlinking converter; hybrid system with DC flexible demand; and hybrid system with SOPs. Results show that the proposed scheduling method can successfully dispatch the controllable elements; and that the presented architecture for the AC/DC hybrid distribution system is beneficial for reducing operating cost and renewable generation curtailment. Full article
Figures

Figure 1

Open AccessArticle Wind Power Development and Energy Storage under China’s Electricity Market Reform—A Case Study of Fujian Province
Sustainability 2018, 10(2), 298; https://doi.org/10.3390/su10020298
Received: 20 December 2017 / Revised: 19 January 2018 / Accepted: 20 January 2018 / Published: 24 January 2018
Cited by 2 | PDF Full-text (7533 KB) | HTML Full-text | XML Full-text
Abstract
This paper, based on the Fujian provincial 500 kV grid and part of the 220 kV grid and the key power plants, including hydro, coal, nuclear, gas, wind and pumping and storage hydro powers (PSHP) connected to the grid, constructs an independent electricity [...] Read more.
This paper, based on the Fujian provincial 500 kV grid and part of the 220 kV grid and the key power plants, including hydro, coal, nuclear, gas, wind and pumping and storage hydro powers (PSHP) connected to the grid, constructs an independent electricity market model. Using data that are very close to reality about coal fired power production costs, along with data about power plants’ technical constraints, this paper studies the effect of wind power on Fujian’s provincial electricity market. Firstly, the paper analyzes the relationship between wind speed and wind power output and the effects of short-term power output fluctuation on frequency modulation and voltage regulation. Secondly, under supposition of the production costs following quadratic functions, the paper analyzes the effects of changes in wind power output on the electricity supply costs under optimal power flow. Thirdly, using the bidding model in the Australian Electricity Market Operator for reference and supposing that, in a competitive market, coal fired power plants can bid 6 price bands according to their capacity, the paper analyzes effects of wind power on electricity prices under optimal power flow, the stabilizing effects of PSHP and the minimum PSHP capacity needed to stabilize the electricity market. Finally, using a daily load curve, this paper simulates the electricity prices’ fluctuation under optimal power flow and PSHP’s stabilizing effect. The results show that, although PSHP has a large external social welfare effect, it can hardly make a profit. In the end, this paper puts forward some policy suggestions for Fujian province’s wind and nuclear power development, PSHP construction and electricity market development. Full article
Figures

Figure 1

Open AccessArticle GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting
Sustainability 2018, 10(1), 217; https://doi.org/10.3390/su10010217
Received: 14 December 2017 / Revised: 10 January 2018 / Accepted: 15 January 2018 / Published: 16 January 2018
Cited by 1 | PDF Full-text (1732 KB) | HTML Full-text | XML Full-text
Abstract
With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. [...] Read more.
With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH) technology. The proposed algorithm consists of three main stages: (1) training the basic classifier; (2) selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3) training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection) and GMDH-U (GMDH-based semi-supervised feature selection for customer classification) models. Full article
Figures

Figure 1

2017

Jump to: 2019, 2018

Open AccessArticle A Stochastic Optimization Model for Carbon Mitigation Path under Demand Uncertainty of the Power Sector in Shenzhen, China
Sustainability 2017, 9(11), 1942; https://doi.org/10.3390/su9111942
Received: 20 September 2017 / Revised: 14 October 2017 / Accepted: 19 October 2017 / Published: 26 October 2017
PDF Full-text (776 KB) | HTML Full-text | XML Full-text
Abstract
In order to solve problems caused by climate change, countries around the world should work together to reduce GHG (greenhouse gas) emissions, especially CO2 emissions. Power demand takes up the largest proportion of final energy demand in China, so the key to [...] Read more.
In order to solve problems caused by climate change, countries around the world should work together to reduce GHG (greenhouse gas) emissions, especially CO2 emissions. Power demand takes up the largest proportion of final energy demand in China, so the key to achieve its goal of energy-saving and emission reduction is to reduce the carbon emissions in the power sector. Taking Shenzhen as an example, this paper proposed a stochastic optimization model incorporating power demand uncertainty to plan the carbon mitigation path of power sector between 2015 and 2030. The results show that, in order to achieve the optimal path in Shenzhen’s power sector, the carbon mitigation technologies of existing coal and gas-fired power plants will be 100% implemented. Two-thirds and remaining one-third of coal-fired power plant capacities are going to be decommissioned in 2023 and 2028, respectively. Gas-fired power, distributed photovoltaic power, waste-to-energy power and CCHP (Combined Cooling, Heating, and Power) are going to expand their capacities gradually. Full article
Figures

Figure 1

Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top