scholarly journals A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3258 ◽  
Author(s):  
Feihu Hu ◽  
Xuan Feng ◽  
Hui Cao
Keyword(s):  
Robust Optimization ◽  
Demand Response ◽  
Electricity Market ◽  
Decision Model ◽  
Expected Profit ◽  
Optimization Approach ◽  
Short Term ◽  
Spot Prices ◽  
Retail Prices ◽  
Time Of Use Pricing

This paper establishes a short-term decision model, based on robust optimization, for an electricity retailer to determine the electricity procurement and electricity retail prices. The electricity procurement process includes purchasing electricity from generation companies and from the spot market. The selling prices of electricity for the customers are based on time-of-use (TOU) pricing which is widely employed in modern electricity market as a demand response program. The objective of the model is to maximize the expected profit of the retailer through optimizing the electricity procurement strategy and electricity pricing scheme. A price elasticity matrix (PEM) is adopted to model the demand response. Also, uncertainty in spot prices is modeled using a robust optimization approach, in which price bounds are considered instead of predicted values. Using a robust optimization approach, the retailer can adjust the level of robustness of its decisions through a robust control parameter. A case study is presented to illustrate the performance of the model. The simulation results demonstrate that the developed model is effective in increasing the expected profit of the retailer and flattening the load profiles of customers.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

Integration of Price-Based Demand Response in DisCos' Short-Term Decision Model

2014 ◽  
Vol 5 (5) ◽  
pp. 2235-2245 ◽  
Author(s):  
Amir Safdarian ◽  
Mahmud Fotuhi-Firuzabad ◽  
Matti Lehtonen
Keyword(s):  
Demand Response ◽  
Decision Model ◽  
Short Term

scholarly journals Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2867 ◽  
Author(s):  
Zhanle Wang ◽  
Raman Paranjape ◽  
Zhikun Chen ◽  
Kai Zeng
Keyword(s):  
Power System ◽  
Demand Response ◽  
Electricity Market ◽  
Energy Cost ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
System Stability ◽  
Optimization Approach ◽  
Model Case ◽  
Multi Agent

Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.

Optimal Scheduling of a Virtual Power Plant with Demand Response in Short-Term Electricity Market

2020 ◽  
Author(s):  
Homa Rashidizadeh-Kermani ◽  
Mostafa Vahedipour-Dahraie ◽  
Miadreza Shafie-khah ◽  
Gerardo J. Osorio ◽  
Joao P. S. Catalao
Keyword(s):  
Power Plant ◽  
Demand Response ◽  
Electricity Market ◽  
Optimal Scheduling ◽  
Virtual Power ◽  
Short Term ◽  
Virtual Power Plant

scholarly journals Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

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