Development of Vehicle-to-Grid System to Regulate the System Parameters of Microgrid

As installed capacity of Chinese renewable energy (RE) power generation has been expanded rapidly, the gap between subsidies and funds is more and more huge. Chinese government has urgently sought other incentive policies in place of subsidy. Considering incentive policies such as RE quota scheme, green certificate trade, and subsidy in scenarios, the performances of RE power generations in the liberalized power market are measured in this article. Centralized renewable energy (CRE) and distributed renewable energy (DRE) generations are distinguished in this study. The results show that even if the subsidy is canceled, there will be no significant reversal of RE power generation. It is best to terminate subsidies after 48 months. With the interaction of the incentive policies, the on-grid market price is rising initially and then falling. The price of tradable green certificate (TGC) fluctuated with electricity demand. Subsidy influences the demand in the TGC market by affecting the amount of power generated by DRE, thereby affecting the profit of the CRE power generation. In addition, the changes in RE quota not only keep the price of TGC at a stable level, but also make the installed capacity of CRE power generation exceed the installed capacity of traditional energy power generation after 80 months.

Feed-in tariffs (FIT) is good for renewable energy (RE) power producers, expanding the scale of RE's development.However, it is unfavorable to RE power grid companies' acquisition, so leading to a serious phenomenon of abandoned wind, solar and water energy.Now this article introduces Quota System, that is Renewable Portfolio Standard (RPS), combining with relevant supporting policies to inspire the RE power producers and power grid companies to carry out the Full Acquisition police of Renewable Energy.

The design of Renewable Energy Power Plants (REPPs) is crucial not only for the investments' performance and attractiveness measures, but also for the maximization of resource (source) usage (e.g. sun, water, and wind) and the minimization of raw materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te) consumption. Hence, several appropriate and satisfactory Multi-Objective Problems (MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only be managed by very well organized knowledge acquisition on all REPPs' design equations and models. The proposed MOPs need to be solved with one or more multi-objective algorithm, such as Multi-Objective Evolutionary Algorithms (MOEAs). In this respect, the first aim of this research study is to start gathering knowledge on the REPPs' MOPs. The second aim of this study is to gather detailed information about all MOEAs and available free software tools for their development. The main contribution of this research is the initialization of a proposed multi-objective evolutionary algorithm knowledge acquisition system for renewable energy power plants (MOEAs-KAS-F-REPPs) (research and development loopwise process: develop, train, validate, improve, test, improve, operate, and improve). As a simple representative example of this knowledge acquisition system research with two selective and elective proposed standard objectives (as test objectives) and eight selective and elective proposed standard constraints (as test constraints) are generated and applied as a standardized MOP for a virtual small hydropower plant design and investment. The maximization of energy generation (MWh) and the minimization of initial investment cost (million €) are achieved by the Multi-Objective Genetic Algorithm (MOGA), the Niched Sharing Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all proposed standardized MOEAs on two desktop computer configurations (Windows 10 Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB RAM with internet connection). The algorithm run-times (computation time) of the current applications vary between 20.64 and 59.98 seconds.

The Indonesian government program in providing solutions for electrical energy distribution problems to get to remote or isolated areas is to optimize the potential of renewable energy in an area. The combination of conventional power plants (diesel generators) with renewable energy (photovoltaic and wind turbine) power plants is expected to solve the problem of electricity service in isolated areas in southern Tulungagung regency, namely residential area in Brumbun Beach. The existence of government assistance in the form of solar panels distributed to each family head still can not optimize the utilization of electrical energy for 24 hours in a day, this is because the generation of diesel generators and solar panels are done separately. This research focuses on the design and analysis of a renewable autonomous power supply system which consists of a centralized solar-powered diesel generator (solar panel – a wind turbine – diesel generator) systems using HOMER software. This software is in addition to being used to create designs, is also capable of performing the most optimal system design evaluation by sorting based on overall cost, basic electricity tariff, and carbon dioxide gas emissions. The research from the design of four power plant configurations shows that the use of 10 kW diesel generators, 8 kW solar panels, and 6 kW wind turbines is the best solution, from the combination of the three energy sources shows the net present cost (NPC) value of US $ 44,680, Cost of Energy (COE) of 0.268 kWh / $, CO2 emissions of 1,077 kg/year, and diesel generator use only 54 minutes a day.Keywords: Cost of Energy, Hybrid Power Generation, HOMER, Net Present Cost, Renewable EnergyJEL Classifications: C63, C88, Q42DOI: https://doi.org/10.32479/ijeep.9066

The present study was conducted to investigate key drivers, barriers, and challenges towards Krabi's sustainable energy. The provincial electricity demand was found to be continuously increasing, while its supply has relied on national grid which is not a sustainable practice. Krabi Goes Green roadmap has set a target to be self-reliance and 100% electricity from renewable energy (RE). This is a key driver for Krabi's RE transition. Even though high RE resources potential was reported, installation of RE power plants can supply only about half of its annual demand. What would be key barriers and challenges to overcome the barriers were investigated via stakeholders’ interviews. Challenges or key success recommendation for each RE are briefly described. Krabi has high potential of both biomass and biogas from wastes of palm oil industry in the province. By the way, most of biomass and biogas power plants are VSPPs having non-firm-type power purchasing agreement (PPA) leading to unsecure electricity buying and selling. The PPA reconsidering was recommended by interviewees. In addition, new biomass power plants are public opposed due to emissions of solid biomass combustion pollutants. Waste-to-energy (WtE) is also high potential, but facing with public opposing as well. Some interviewees suggested installation of high efficiency end-of-pipe treatment facilities together with real-monitoring system must be compulsory for both biomass and WtE power plants. Solar PV is also high potential, but facing a big barrier of land limitation for solar farm installation. Therefore, solar rooftop has become the only option, but facing with a quota system. Reconsideration on the quota system policy as well as more simplify and transparent approval process are highly recommended for promoting solar energy both in the province and the whole country.

Purpose In the hybrid electricity market, renewable energy power generator faces the uncertainty of power market demand and the randomness of the renewable energy generation output. In order to improve the grid-connected quantity of green power, the purpose of this paper is to design the pricing mechanism for renewable energy power generator with revenue-sharing contract in a two-stage “multi-single” electricity supply chain which contains a single dominant power retailer and two kinds of power suppliers providing different power energy species. Design/methodology/approach Considering the dual uncertainties of renewable energy power output and power market demand, the authors design the full-cooperative contract decision-making model, wholesale price contract decision-making model and revenue-sharing contract decision-making model to compare and optimize grid-connected pricing in order to maximize profit of different parties in power supply chain. Then, this paper performs a numerical simulation, discusses the existence of the equilibrium analytical solutions to satisfy the supply chain coordination conditions and analyzes the optimal contract parameters’ variation characteristics and their interaction relationship. Findings The authors find that the expected profits of the parties in the hybrid power supply chain are concave about their decision variables in each decision-making mode. The revenue-sharing contract can realize the Pareto improvement for all parties’ interest of the supply chain, and promote the grid-connected quantity of green power effectively. The grid-connected price will reduce with the increase of revenue-sharing ratio, and this impact will be greater on the renewable energy power. The greater the competition intensity in power supply side, the smaller the revenue-sharing ratio from power purchaser. And for the same rangeability of competition intensity, the revenue-sharing ratio reduction of thermal power is less than that of the green power. The more the government subsidizing green power supplier, the smaller the retailer sharing revenue to it. Practical implications Facing with the dual uncertainties of green power output and market demand and the competition of thermal power in hybrid electricity market, this study can provide a path to solve the problem of renewable energy power grid-connecting. The results can help green power become competitive in hybrid power market under loose regulations. And this paper suggests that the government subsidy policy should be more tactical in order to implement a revenue-sharing contract of the power supply chain. Originality/value This paper studies the renewable energy electricity grid-connected pricing under the uncertainty of power supply and market demand, and compares different contract decision-making strategies in order to achieve the power supply chain coordination. The paper also analyzes the competition between thermal power and renewable energy power in hybrid electricity market.

Government power plants cannot supply all electricity demand in East Kalimantan Province especially if there is damage to the power plant with the largest production capacity, so cooperation with other suppliers needs to be done. There are four stakeholders that can supply electricity, namely State Electricity Company (SEC), Independent Power Producer (IPP), Rent, and Excess Capacity (EC). Each supplier has different power generation characteristics that lead to differences in fuel costs and emissions. This research proposes 4 mathematical models. The models are scenarios 4 forms of cooperation between electrical stakeholders in East Kalimantan taking into consideration the potential of renewable energy (RE) power plants. This mathematical model is solved with a heuristic algorithm solver excel. The result, the best scenario by integrating three power plants owned by SEC, IPP and RE. Whereas collaboration with the smallest emissions is SEC + Rent + RE.

A review of flexibility options for high RES penetration in power systems — Focusing the Greek case

Synthetic inertia and frequency support assessment from renewable plants in low carbon grids

To reduce the financial burden brought by the feed-in tariff (FIT) policy, China is shifting its renewable energy support policy to the renewable portfolio standard with tradable green certificates (RPS/TGC). Moreover, the two policies are being implemented in parallel in the transitional period. In this context, this study investigates the game between renewable energy power plants (REPPs) regarding electricity outputs and tradable green certificate (TGC) prices under different renewable energy support policies practiced in China. We focus on analyzing the impact of policy changes on the competing REPPs’ decisions and the renewable electricity market with TGCs. The results show that implementing the RPS/TGC policy inhibits the development of renewable electricity unless the subscription of TGCs is promoted. Government subsidy arrears significantly affect electricity outputs and TGC subscriptions. The low subscription rate of TGCs is caused not by the subsidy-based TGC pricing mechanism but by government subsidies. This suggests that the government should implement the RPS/TGC policy, and demand-side power consumption responsibility weights should also be strengthened to promote TGC subscriptions.

With the steady progress of the energy transition, the proportion of renewable energy in my country has continued to increase, and the market-oriented trading of renewable energy power has become an important link in promoting the construction of Chinese low-carbon clean energy system.Under the background of the new quota system policy, through the analysis of Chinese renewable energy distribution and market-oriented transaction needs and characteristics,research the application method of BlockChain technology in renewable energy power transactions,from the three aspects of transaction system architecture, BlockChain support technology selection, and transaction process management, an intelligent transaction model suitable for Chinese renewable energy power consumption is proposed, building an automatic matching and automatic settlement method for renewable energy power transactions based on BlockChain technology, optimizing the transaction process, and reduce transaction management costs, providing a certain reference for the development of Chinese renewable energy power market trading.

Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal fusion-based predictive tool for energy consumption prediction (MDLFM-ECP) of RE and NRE power sources. Actual data may influence the prediction performance of the results in prediction approaches. The proposed MDLFM-ECP technique involves pre-processing, fusion-based prediction, and hyperparameter optimization. In addition, the MDLFM-ECP technique involves the fusion of four deep learning (DL) models, namely long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), deep belief network (DBN), and gated recurrent unit (GRU). Moreover, the chaotic cat swarm optimization (CCSO) algorithm is applied to tune the hyperparameters of the DL models. The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models, showing the novelty of the work. A series of simulations took place to validate the superior performance of the proposed method, and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

The large-scale renewable energy power plants connected to a weak grid may cause bus voltage fluctuations in the renewable energy power plant and even power grid. Therefore, reactive power compensation is demanded to stabilize the bus voltage and reduce network loss. For this purpose, time-series characteristics of renewable energy power plants are firstly reflected using K-means++ clustering method. The time group behaviors of renewable energy power plants, spatial behaviors of renewable energy generation units, and a time-and-space grouping model of renewable energy power plants are thus established. Then, a mixed-integer optimization method for reactive power compensation in renewable energy power plants is developed based on the second-order cone programming (SOCP). Accordingly, power flow constraints can be simplified to achieve reactive power optimization more efficiently and quickly. Finally, the feasibility and economy for the proposed method are verified by actual renewable energy power plants.

China introduced the Renewable Portfolio Standard (RPS) in 2019 to boost renewable energy (RE) consumption across provinces, yet the policy overlooks associated employment impacts. To fill this gap, this paper constructs an inter-provincial cooperation model for non-hydro RE power consumption that integrates a dual-objective optimization (minimizing quota completion cost and maximizing employment) and a minimum-deviation benefit allocation approach. The model identifies the roles of green power buyers and sellers, and quantifies optimal RE generation, employment, and costs under both non-cooperation and cooperation scenarios. An empirical analysis is conducted for five representative provinces (Hebei, Yunnan, Jiangxi, Anhui, and Hunan). The results indicate that, compared with the non-cooperation mode, inter-provincial cooperation increases non-hydro RE power generation by 13.42 × 10⁹ kWh (27.4%), creates an additional 1.39 × 105 jobs, and increases total comprehensive benefits by 3.38 × 10⁹ CNY (9.9%). The proposed minimum-deviation allocation method achieves fairer benefit distribution than single methods such as the Shapley value and game quadratic programming. This study provides a practical framework for provincial governments to balance RE consumption, cost efficiency, and employment growth in cross-regional green power cooperation.

To reach Carbon Peak in 2030 and Carbon Neutrality in 2060, China is developing renewable energy at a fast pace. Renewable energy enterprises will participate in the power market in an all-round way as China gradually improves its electricity market. Signing the Power Purchase Agreement (PPA) helps renewable energy companies to avoid market risk and achieve sustainable development. Therefore, a novel PPA pricing model is proposed in our research. Based on the theory of the Levelized Cost of Energy (LCOE), our model considers system operating costs in China’s dual-track electric power sector, which is both government-guided and market-oriented. First of all, key influencing factors of the PPA agreement are analyzed in view of the developments of the renewable energy and electricity markets in China. Next, the design of pricing strategies for renewable energy power plants to cope with market challenges is presented through a photovoltaic project case study. The results show that when the operating costs of the system are considered and other conditions remain unchanged, the investment payback period of the new energy power station will change from 10.8 years to 13.6 years. Furthermore, correlation degree and sensitivity coefficient (SAF) were introduced to conduct correlation analysis and sensitivity analysis of key elements that affect the pricing of the PPA. Finally, it is concluded that the utilization hours of power generation have the most significant effect on the PPA price, while the system’s operating cost is the least sensitive factor. The study expands the application of LCOE, and provides a decision-making solution for the PPA pricing of renewable energy power enterprises. It is expected to help promote power transactions by renewable energy companies.