scholarly journals Integrating Demand Response and Renewable Energy In Wholesale Market

2018 ◽  
Author(s):  
Chaojie Li ◽  
Chen Liu ◽  
Xinghuo Yu ◽  
Ke Deng ◽  
Tingwen Huang ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Demand Response ◽  
Electricity Market ◽  
Stochastic Game ◽  
Renewable Energy Sources ◽  
Game Model ◽  
Renewable Energy Resources ◽  
Wholesale Market ◽  
Policy Makers ◽  
Optimal Energy

 Demand response (DR) can provide a cost-effect approach for reducing peak loads while renewable energy sources (RES) can result in an environmental-friendly solution for solving the problem of power shortage. The increasingly integration of DR and renewable energy bring challenging issues for energy policy makers, and electricity market regulators in the main power grid. In this paper, a new two-stage stochastic game model is introduced to operate the electricity market, where Stochastic Stackelberg-Cournot-Nash (SSCN) equilibrium  is applied to characterize the optimal energy bidding strategy of the forward market and the optimal energy trading strategy of the spot market. To obtain a SSCN equilibrium, sampling average approximation (SAA) technique is harnessed to address the stochastic game model in a distributed way. By this game model, the participation ratio of demand response can be significantly increased while the unreliability of power system caused by renewable energy resources can be considerably reduced. The effectiveness of proposed model is illustrated by extensive simulations.

scholarly journals Demand-Side Management of Smart Distribution Grids Incorporating Renewable Energy Sources

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 143 ◽  
Author(s):  
Gerardo J. Osório ◽  
Miadreza Shafie-khah ◽  
Mohamed Lotfi ◽  
Bernardo J. M. Ferreira-Silva ◽  
João P. S. Catalão
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Demand Response ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Demand Side Management ◽  
Pollutant Emissions ◽  
Demand Side ◽  
Renewable Energy Resources ◽  
Distribution Grids

The integration of renewable energy resources (RES) (such as wind and photovoltaic (PV)) on large or small scales, in addition to small generation units, and individual producers, has led to a large variation in energy production, adding uncertainty to power systems (PS) due to the inherent stochasticity of natural resources. The implementation of demand-side management (DSM) in distribution grids (DGs), enabled by intelligent electrical devices and advanced communication infrastructures, ensures safer and more economical operation, giving more flexibility to the intelligent smart grid (SG), and consequently reducing pollutant emissions. Consumers play an active and key role in modern SG as small producers, using RES or through participation in demand response (DR) programs. In this work, the proposed DSM model follows a two-stage stochastic approach to deal with uncertainties associated with RES (wind and PV) together with demand response aggregators (DRA). Three types of DR strategies offered to consumers are compared. Nine test cases are modeled, simulated, and compared in order to analyze the effects of the different DR strategies. The purpose of this work is to minimize DG operating costs from the Distribution System Operator (DSO) point-of-view, through the analysis of different levels of DRA presence, DR strategies, and price variations.

scholarly journals Acceptance of Demand Response and Aggregators as a Solution to Optimize the Relation between Energy Producers and Consumers in Order to Increase the Amount of Renewable Energy in the Grid

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3441
Author(s):  
Adrian Tantau ◽  
András Puskás-Tompos ◽  
Laurentiu Fratila ◽  
Costel Stanciu
Keyword(s):  
Renewable Energy ◽  
Demand Response ◽  
Electricity Market ◽  
Online Survey ◽  
Renewable Energy Sources ◽  
Electrical Energy ◽  
Structured Interviews ◽  
New Development ◽  
Demand Response Programs

Demand response plays a very important role in balancing the intermittent production of an increasing share of renewable energy sources on the energy market. This article analyses the importance of demand response and the role of aggregators for the new development of the electricity market, where the renewables will play a more important role. The main objective of this research is to determine the acceptance level of demand response and its implementation on the energy consumer side. This acceptance should include a professional actor, the aggregator which is assuming the role of optimizing the relation between energy producers and consumers, and to monitor the implementation and use of demand response. The research is based on semi-structured interviews with experts in energy from Hungary, Romania and Serbia, on workshops with experts and a wider online survey with end customers for electricity. The results indicate that there is a willingness potential to implement demand response programs with aggregators as intermediaries between energy providers and end consumers of electrical energy.

scholarly journals Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
V. V. S. N. Murty ◽  
Ashwani Kumar
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Optimal Energy ◽  
Simulation Results ◽  
Battery Energy ◽  
Microgrid Energy Management

AbstractMicrogrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixed-integer linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it’s impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it’s effectiveness.

scholarly journals Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3860
Author(s):  
Priyanka Shinde ◽  
Ioannis Boukas ◽  
David Radu ◽  
Miguel Manuel de Manuel de Villena ◽  
Mikael Amelin
Keyword(s):  
Renewable Energy ◽  
Electricity Market ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Agent Based Modeling ◽  
Market Liquidity ◽  
Energy Sources ◽  
Modeling Framework ◽  
Agent Based

In recent years, the vast penetration of renewable energy sources has introduced a large degree of uncertainty into the power system, thus leading to increased trading activity in the continuous intra-day electricity market. In this paper, we propose an agent-based modeling framework to analyze the behavior and the interactions between renewable energy sources, consumers and thermal power plants in the European Continuous Intra-day (CID) market. Additionally, we propose a novel adaptive trading strategy that can be used by the agents that participate in CID market. The agents learn how to adapt their behavior according to the arrival of new information and how to react to changing market conditions by updating their willingness to trade. A comparative analysis was performed to study the behavior of agents when they adopt the proposed strategy as opposed to other benchmark strategies. The effects of unexpected outages and information asymmetry on the market evolution and the market liquidity were also investigated.

scholarly journals Transposition of European Guidelines for Energy Communities into Austrian Law: A Comparison and Discussion of Issues and Positive Aspects

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3922
Author(s):  
Bernadette Fina ◽  
Hubert Fechner
Keyword(s):  
Renewable Energy ◽  
Electricity Market ◽  
Clean Energy ◽  
Detailed Comparison ◽  
Regulatory Framework ◽  
Policy Makers ◽  
European Guidelines ◽  
Subsequent Discussion ◽  
Grid Operators ◽  
Austrian Law

The Renewable Energy Directive and the Electricity Market Directive, both parts of the Clean Energy for all Europeans Package (issued in 2019), provide supranational rules for renewable energy communities and citizen energy communities. Since national transpositions need to be completed within two years, Austria has already drafted corresponding legislation. This article aims at providing a detailed comparison of the European guidelines and the transposition into Austrian law. The comparison not only shows how, and to what extent, the European guidelines are transposed into Austrian law, but also helps to identify loopholes and barriers. The subsequent discussion of these issues as well as positive aspects of the Austrian transposition may be advantageous for legislators and policy makers worldwide in their process of designing a coherent regulatory framework. It is concluded that experts from different areas (i.e., project developers, scientists concerned with energy communities, energy suppliers and grid operators) should be closely involved in the law-making process in order to introduce different perspectives so that a consistent and supportive regulatory framework for energy communities is created.

scholarly journals Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2700
Author(s):  
Grace Muriithi ◽  
Sunetra Chowdhury
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Critical Role ◽  
Winter Season ◽  
Learning Approach ◽  
Solar Pv ◽  
Optimal Energy ◽  
Optimal Energy Management

In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season.

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