scholarly journals Research on Decision Optimization Model of Microgrid Participating in Spot Market Transaction

2021 ◽  
Vol 13 (12) ◽  
pp. 6577
Author(s):  
Jun Dong ◽  
Yuanyuan Wang ◽  
Xihao Dou ◽  
Zhengpeng Chen ◽  
Yaoyu Zhang ◽  
...  
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Peak Load ◽  
Market Price ◽  
Optimization Method ◽  
Spot Market ◽  
Economic Benefits ◽  
Decision Optimization ◽  
Market Transaction ◽  
Expected Revenue

The development of electricity spot trading provides an opportunity for microgrids to participate in the spot market transaction, which is of great significance to the research of microgrids participating in the electricity spot market. Under the background of spot market construction, this paper takes the microgrid including wind power, photovoltaic (PV), gas turbine, battery storage, and demand response as the research object, uses the stochastic optimization method to deal with the uncertainty of wind and PV power, and constructs a decision optimization model with the goal of maximizing the expected revenue of microgrids in the spot market. Through the case study, the optimal bidding electricity of microgrid operators in the spot market is obtained, and the revenue is USD 923.07. Then, this paper further investigates the effects of demand response, meteorological factors, market price coefficients, and cost coefficients on the expected revenue of microgrids. The results demonstrate that the demand response adopted in this paper has better social–economic benefits, which can reduce the peak load while ensuring the reliability of the microgrid, and the optimization model also ensure profits while extreme weather and related economic coefficients change, providing a set of scientific quantitative analysis tools for microgrids to trade electricity in the spot market.

scholarly journals Towards the Interactive Effects of Demand Response Participation on Electricity Spot Market Price

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Saeed Mohajeryami ◽  
Milad Doostan ◽  
Seyedmahdi Moghadasi ◽  
Peter Schwarz
Keyword(s):  
Regression Model ◽  
Demand Response ◽  
Market Price ◽  
Multiple Linear Regression Model ◽  
Price Volatility ◽  
Spot Market ◽  
Positive Effects ◽  
Electricity Cost ◽  
Electricity Spot Market ◽  
Price Spikes

Abstract The electricity market is threatened by supply scarcity, which may lead to very sharp price spikes in the spot market. On the other hand, demand-side’s activities could effectively mitigate the supply scarcity and absorb most of these shocks and therefore smooth out the price volatility. In this paper, the positive effects of employing demand response programs on the spot market price are investigated. A demand-price elasticity based model is used to simulate the customer reaction function in the presence of a real time pricing. The demand achieve by DR program is used to adjust the spot market price by using a price regression model. SAS software is used to run the multiple linear regression model and MATLAB is used to simulate the demand response model. The approach is applied on one week data in summer 2014 of Connecticut in New England ISO. It could be concluded from the results of this study that applying DR program smooths out most of the price spikes in the electricity spot market and considerably reduces the customers’ electricity cost.

scholarly journals Application of Lagrange Relaxation to Decentralized Optimization of Dispatching a Charging Station for Electric Vehicles

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 288 ◽  
Author(s):  
Shan Cheng ◽  
Yichen Feng ◽  
Xianning Wang
Keyword(s):  
Electric Vehicles ◽  
Optimization Model ◽  
Large Scale ◽  
Peak Load ◽  
Relaxation Method ◽  
Optimization Method ◽  
Decentralized Optimization ◽  
Lagrange Relaxation ◽  
Charging Station ◽  
Computation Efficiency

To improve the computation efficiency of optimally dispatching large-scale cluster electric vehicles (EVs) and to enhance the profit of a charging station (CS) for EVs, this study investigates the optimal dispatch of the CS based on a decentralized optimization method and a time-of-use (TOU) price strategy. With the application of the Lagrange relaxation method (LRM), a decentralized optimization model with its solution is proposed that converts the traditional centralized optimization model into certain sub-problems. The optimization model aims to maximize the profit of CS, but it comprehensively considers the charging preference of EV users, the operation constraints of the distribution network, and the TOU strategy adopted by the CS. To validate the proposed decentralized optimal dispatching method, a series of numerical simulations were conducted to demonstrate its effect on the computation efficiency and stability, the profit of the CS, and the peak-load shifting. The result indicates that the TOU strategy markedly increases the profit of the CS in comparison with the fixed electricity price mechanism, and the computation efficiency and stability are much better than those of the centralized optimization method. Although it does not compensate the load fluctuation completely, the proposed method with the TOU strategy is helpful for filling the valley of power use.

Demand Response and FERC Mandated Compensation Issues

2012 ◽  
Author(s):  
Donald Lincoln
Keyword(s):  
Energy Efficiency ◽  
Demand Response ◽  
Market Competition ◽  
Peak Load ◽  
Market Price ◽  
Energy Markets ◽  
Local Market ◽  
Wholesale Market ◽  
Market Prices ◽  
Demand Response Programs

This paper describes a Demand Response (DR) pilot event performed at Sandia National Laboratories in August of 2011. This paper includes a description of the planning for the demand response event, sources of energy reduction during the event, the potential financial benefit to Sandia National Laboratories from the event, event implementation issues, and the event results. In addition, this paper presents the implications of the Federal Energy Regulatory Commission (FERC) Order 745, Demand Response Compensation in Organized Wholesale Energy Markets, issued in March 2011. In this order FERC mandates that demand response suppliers must be compensated by the organized wholesale energy markets at the local market price for electricity during the hour the demand response is performed. Energy management in a commercial facility can be segregated into energy efficiency and demand response. Energy efficiency focuses on steady state load minimization. Demand response reduces load for event-driven periods during the peak load. Commercial facility demand response refers to voluntary actions by customers that change their consumption of electric power in response to price signals, incentives, or directions from grid operators at times of high wholesale market prices or when electric system reliability is jeopardized. Demand-response-driven changes in electricity use are designed to be short-term and centered on critical hours during the day when demand is high or when the electricity supplier’s reserve margins are low. Demand response events are typically scheduled between 12:00 p.m. and 7:00 p.m. on eight to 15 days during the hottest period of the year. Analysis has determined that automated demand response programs are more efficient and effective than manually controlled demand response programs due to persistence. FERC has stated that their Order 745 ensures organized wholesale energy market competition and removes barriers to the participation of demand response resources. In Order 745, FERC also directed that the demand response compensation costs be allocated among those customers who benefit from the lower prices for energy resulting from the demand response. FERC has allowed the organized wholesale energy markets to establish details for implementation methods for demand response compensation over the next four years following the final Order issue date. This compensation to suppliers of demand response can be significant since demand response is typically performed during those hours when the wholesale market prices are at their highest levels during the year.

scholarly journals Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 452 ◽  
Author(s):  
Sung-Ho Park ◽  
Akhtar Hussain ◽  
Hak-Man Kim
Keyword(s):  
Demand Response ◽  
Impact Analysis ◽  
Peak Load ◽  
Market Price ◽  
Load Level ◽  
Load Shedding ◽  
Case Scenario ◽  
Worst Case ◽  
The Impact ◽  
Operation Case

Microgrids have the potential to withstand the power outages due to their ability of islanding and potential to sustain the penetration of renewables. Increased penetration of renewables can be beneficial but it may result in curtailment of renewables during peak generation intervals due to the limited availability of storage capacity while shedding loads during peak load intervals. This problem can be solved by adjusting the load profiles, i.e., demand response (DR) programs. In contrast to the existing studies, where DR is triggered by market price signals, a local resource-triggered survivability-oriented demand response program is proposed in this paper. The proposed DR program is triggered by renewable and load level of the microgrid with an objective to minimize the load shedding and curtailment of renewables. The uncertainties in load and renewables are realized via a robust optimization method and the worst-case scenario is considered. The performance of the proposed method is compared with two conventional operation cases, i.e., independent operation case and interconnected operation case without DR. In addition, the impact of renewable penetration level, amount of shiftable load, and load absorption capacity on the performance of the proposed method are also analyzed. Simulation results have proved the proposed method is capable of reducing load shedding, renewable curtailment, and operation cost of the network during emergencies.

scholarly journals Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3701 ◽  
Author(s):  
Haesum Ali ◽  
Akhtar Hussain ◽  
Van-Hai Bui ◽  
Jinhong Jeon ◽  
Hak-Man Kim
Keyword(s):  
Demand Response ◽  
Peak Load ◽  
Market Price ◽  
Consensus Method ◽  
Welfare Maximization ◽  
Total Surplus ◽  
Load Demand ◽  
Price Signals ◽  
Demand Response Program ◽  
Demand Response Programs

Integration of demand response programs in microgrids can be beneficial for both the microgrid owners and the consumers. The demand response programs are generally triggered by market price signals to reduce the peak load demand. However, during islanded mode, due to the absence of connection with the utility grid, the market price signals are not available. Therefore, in this study, we have proposed a distributed demand response program for an islanded multi-microgrid network, which is not triggered by market price signals. The proposed distributed demand response program is based on welfare maximization of the network. Based on the welfare function of individual microgrids, the optimal power is allocated to the microgrids of the network in two steps. In the first step, the total surplus power and shortage power of the network is determined in a distributed way by using the local surplus/shortage information of each microgrid, which is computed after local optimization. In the second step, the total surplus of the network is allocated to the microgrids having shortage power based on their welfare functions. Finally, the allocated power amount and the initial shortage amount in the microgrid is used to determine the amount of load to be curtailed. Diffusion strategy is used in both the first and the second steps and the performance of the proposed method is compared with the widely used consensus method. Simulation results have proved the effectiveness of the proposed method for realizing distributed demand response for islanded microgrid networks.

scholarly journals A Two-Stage Dispatch Mechanism for Virtual Power Plant Utilizing the CVaR Theory in the Electricity Spot Market

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3402 ◽  
Author(s):  
Rui Gao ◽  
Hongxia Guo ◽  
Ruihong Zhang ◽  
Tian Mao ◽  
Qianyao Xu ◽  
...  
Keyword(s):  
Power Plant ◽  
Demand Response ◽  
Market Price ◽  
Power Market ◽  
Spot Market ◽  
Virtual Power ◽  
Two Stage ◽  
Virtual Power Plant ◽  
Electricity Spot Market ◽  
The Impact

The electricity spot market is now being implemented in China. Demand response, as a kind of flexible resource, is also being studied and explored for the constructed power market. Among the many demand response applications, the virtual power plant (VPP) as an aggregator of distributed energy resources (DERs), receives ever-increasing attention. However, the participation manner and related impacts of the VPP to the electricity spot market are still unknown within the current power market rules. Under this background, obeying the present trading rules of China’s electricity spot market, a two-stage dispatching model with optimized bidding and operating strategy in the day-ahead (DA) and real-time (RT) market for the VPP is proposed. In the designed model, the conditional risk value (CVaR) is adopted to address the risk encountered by the uncertainty of the electricity spot market price. The impact of the user-side over-deviated revenue mechanism (UORM) of the China spot market on the income of the VPP in the DA and RT market is also analyzed. For a full evaluation, different coefficients for the influence of DA and RT risk, UORM, and energy storage system (ESS) are tested to investigate their respective impacts on the revenue of the VPP. The simulation cases prove that the proposed method is helpful for the VPP to optimize DERs’ output in the electricity spot market according to its own risk preference.

scholarly journals Price Based Demand Response for Optimal Frequency Stabilization in ORC Solar Thermal Based Isolated Hybrid Microgrid under Salp Swarm Technique

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2209
Author(s):  
Abdul Latif ◽  
Manidipa Paul ◽  
Dulal Chandra Das ◽  
S. M. Suhail Hussain ◽  
Taha Selim Ustun
Keyword(s):  
Demand Response ◽  
Frequency Control ◽  
Thermal Power ◽  
Peak Load ◽  
Energy System ◽  
Frequency Stabilization ◽  
Load Frequency Control ◽  
Solar Thermal ◽  
Proportional Integral ◽  
Hybrid Microgrid

Smart grid technology enables active participation of the consumers to reschedule their energy consumption through demand response (DR). The price-based program in demand response indirectly induces consumers to dynamically vary their energy use patterns following different electricity prices. In this paper, a real-time price (RTP)-based demand response scheme is proposed for thermostatically controllable loads (TCLs) that contribute to a large portion of residential loads, such as air conditioners, refrigerators and heaters. Wind turbine generator (WTG) systems, solar thermal power systems (STPSs), diesel engine generators (DEGs), fuel cells (FCs) and aqua electrolyzers (AEs) are employed in a hybrid microgrid system to investigate the contribution of price-based demand response (PBDR) in frequency control. Simulation results show that the load frequency control scheme with dynamic PBDR improves the system’s stability and encourages economic operation of the system at both the consumer and generation level. Performance comparison of the genetic algorithm (GA) and salp swarm algorithm (SSA)-based controllers (proportional-integral (PI) or proportional integral derivative (PID)) is performed, and the hybrid energy system model with demand response shows the supremacy of SSA in terms of minimization of peak load and enhanced frequency stabilization of the system.

scholarly journals An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4398
Author(s):  
Yiqi Li ◽  
Jing Zhang ◽  
Zhoujun Ma ◽  
Yang Peng ◽  
Shuwen Zhao
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Value At Risk ◽  
Operating Cost ◽  
Optimization Method ◽  
Conditional Value At Risk ◽  
Demand Side ◽  
Scheduling Model ◽  
Management Optimization ◽  
Demand Side Response

With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers’ willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users’ willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department.

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