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Special Issue "Power System and Sustainability"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (30 April 2018)

Special Issue Editors

Guest Editor
Dr. Gaetano Zizzo

Department of Energy, Information Engineering and Mathematical Models – University of Palermo, Italy
Website | E-Mail
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

Special Issue 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 special issue 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 monthly 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 1400 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 (17 papers)

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Research

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
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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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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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
(This article belongs to the Special Issue Power System and Sustainability)
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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 1 | 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
(This article belongs to the Special Issue Power System and Sustainability)
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 1 | 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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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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
(This article belongs to the Special Issue Power System and Sustainability)
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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
(This article belongs to the Special Issue Power System and Sustainability)
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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 1 | 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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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
(This article belongs to the Special Issue Power System and Sustainability)
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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
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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
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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
(This article belongs to the Special Issue Power System and Sustainability)
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