Hybrid AC-DC microgrid (HMG) allows direct integration of both AC distributed generators (DGs) and DC DGs, AC and DC loads into the grid. The AC and DC sources and loads are separated out and are connected to respective subgrid mainly to reduce the power conversion; thus the overall efficiency of the system increases. This study aims to introduce a novel HMG planning model within a microgrid market environment to maximise net social welfare (NSW). NSW is defined as the present value of total demand payment minus the present value of total planning cost, including the investment cost of distributed energy sources (DERs) and converters, operation cost of DERs, and the cost of energy exchange with the utility grid subject to network constraints. The scenario tree approach is used to model the uncertainties related to load demand, wind speed, and solar irradiation. The effectiveness of the proposed model is validated through the simulation studies on a 28-bus real HMG.

Renewable energy sources are becoming more prevalent as a source of clean energy, and their integration into the power industry is speeding up. The fundamental reason for this is the growing global concern about climate change. However, the weather-dependent and uncertain nature of renewable generation raise questions about grid security particularly, when photovoltaics (PVs) and wind turbines (WTs) technologies are used. The incorporation of energy storage systems (ESS) in a virtual power plant (VPP) environment could compensate for the renewable generation's uncertainty. Where a VPP is a new concept that combines dispatchable and non-dispatchable energy sources, electrical loads, and energy storage units, enabling individual energy producers to participate in the electricity market. In this study, a market-based-VPP’s operational planning and design model is presented to assess the optimal active power dispatch of (WT, PV, and ESS) operating in the day-ahead electricity market to maximize social welfare (SW) considering the uncertainties associated with wind speed, solar irritation, and load demand. The Scenario-tree technique is applied to model the uncertainties of renewable energy sources and load demand. The proposed model performance is validated by simulation studies on a 16-bus UK generic distribution system (UKGDS). According to the simulation results, renewable energy sources and energy storage systems dispatched optimally active power to satisfy the load demand in the most efficient way possible.

. In this study, a robust optimisation method (ROM) is proposed with aim to achieve optimal scheduling of virtual power plants (VPPs) in the day-ahead electricity markets where electricity prices are highly uncertain. Our VPP is a collection of various distributed energy resources (DERs), flexible loads, and energy storage systems that are coordinated and operated as a single entity. In this study, an offer and bid-based energy trading mechanism is proposed where participating members in the VPP setting can sell or buy to/from the day-ahead electricity market to maximise social welfare (SW). SW is defined as the maximisation of end-users benefits and minimisation of energy costs. The optimisation problem is solved as a mixed-integer linear programming model taking the informed decisions at various levels of uncertainty of the market prices. The benefits of the proposed approach are consistency in solution accuracy and traceability due to less computational burden and this would be beneficial for the VPP operators. The robustness of the proposed mathematical model and method is confirmed in a case study approach using a distribution system with 18-buses. Simulation results illustrate that in the highest robustness scenario, profit is reduced marginally, however, the VPP showed robustness towards the day-ahead market (DAM) price uncertainty

Operational challenges are expected when a large amount of wind and solar energy is added to the electricity networks. It is necessary to introduce new technologies to allow more energy portfolio integration into power systems in order to compensate for the intermittent nature of renewable energy sources (RESs) such as wind and solar power due to their fluctuating nature. A potential solution to the problem of renewable energy integration in power market transactions is the virtual power plant (VPP). A VPP is a novel and smart approach of integrating distributed energy resources (DERs) such as demand response (DR) and energy storage systems (ESS). A VPP could exploit DERs and demand-side participation to mitigate peak loads and thus sustain grid stability. This paper presents a DR strategy of a VPP for simulating energy transactions within the VPP internal electricity market. The method assesses the impact of the DR program on renewable energies integration aiming to minimize VPP operating costs over the short-term planning horizon. Stochastic programming theory is used to address the optimization problem while protecting the interests of the end users. Preliminary findings show that peak load has been reduced while the overall cost of operating has decreased.