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This study proposes a stochastic optimisation model for the day-ahead scheduling in power systems, which incorporates the hourly demand response (DR) for managing the variability of renewable energy sources (RES). DR considers physical and operating constraints of the hourly demand for economic and reliability responses. The proposed stochastic day-ahead scheduling algorithm considers random outages of system components and forecast errors for hourly loads and RES. The Monte Carlo simulation is applied to create stochastic security-constrained unit commitment (SCUC) scenarios for the day-ahead scheduling. A general-purpose mixed-integer linear problem software is employed to solve the stochastic SCUC problem. The numerical results demonstrate the benefits of applying DR to the proposed day-ahead scheduling with variable RES.

Dealing with multi-objective problems has several interesting benefits, one of which is that it supplies the decision-maker with complete information regarding the Pareto front, as well as a clear overview of the various trade-offs that are involved in the problem. The selection of such a representative set is, in and of itself, a multi-objective problem that must take into consideration the number of choices to show the uniformity of the representation and/or the coverage of the representation in order to ensure the quality of the solution. In this study, day-ahead scheduling has been transformed into a multi-objective optimization problem due to the inclusion of objectives, such as the operating cost of multi-energy multi-microgrids (MMGs) and the profit of the Distribution Company (DISCO). The purpose of the proposed system is to determine the best day-ahead operation of a combined heat and power (CHP) unit, gas boiler, energy storage, and demand response program, as well as the transaction of electricity and natural gas (NG). Electricity and gas are traded by MGs with DISCO at prices that are dynamic and fixed, respectively. Through scenario generation and probability density functions, the uncertainties of wind speed, solar irradiation, electrical, and heat demands have been considered. By using mixed-integer linear programming (MILP) for scenario reduction, the high number of generated scenarios has been significantly reduced. The ɛ-constraint approach was used and solved as mixed-integer nonlinear programming (MINLP) to obtain a solution that meets the needs of both of these nonlinear objective functions.

The intermittent nature of distributed energy resources (DERs) has introduced significant challenges in power system operations, particularly in terms of flexibility, efficiency, and market participation. Aggregating DERs into a virtual power plant (VPP) offers a promising solution to these challenges, but it requires effective strategies to manage the inherent uncertainties and optimize operations across multiple energy markets. This paper develops an optimal bidding strategy for an aggregated multienergy virtual power plant (MEVPP) participating in both the day‐ahead (DA) energy market and the frequency regulation reserve market (FRRM). To effectively address these uncertainties, we propose a two‐stage scenario‐oriented stochastic optimization model that aims to maximize revenue and minimize operational costs by incorporating risk management strategies. Then, a novel fast forward selection and simultaneous reduction (FFS&SR) algorithm is proposed, which efficiently generates and refines scenarios, ensuring computational feasibility without compromising accuracy. The proposed VPP’s decision‐making problem considers the VPP’s risk‐averse nature, employing the conditional value at risk (CVaR) metric as a risk‐aversion parameter. Simulation results conducted over a 24‐h planning horizon validate the model’s performance, exhibiting superior performance in the bidding market scenarios. Furthermore, the numerical findings compare the risk‐neutral VPP framework with the proposed risk‐sensitive VPP strategy, revealing a trade‐off between expected profit and CvaR, indicating that as the risk aversion parameter escalates, expected profits decline while CVaR value rises, underscoring the importance of risk management in VPP optimization.