This paper addresses the bidding problem faced by a virtual power plant (VPP) in a joint market of energy and spinning reserve service. The proposed bidding strategy is a non-equilibrium model based on the deterministic price-based unit commitment (PBUC) which takes the supply-demand balancing constraint and security constraints of VPP itself into account. The presented model creates a single operating profile from a composite of the parameters characterizing each distributed energy resources (DER), which is a component of VPP, and incorporates network constraints into its description of the capabilities of the portfolio. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and solved by genetic algorithm (GA).

This paper is to evaluate the presented model in part I for bidding strategy of virtual power plant (VPP) with centralized control in a joint market of energy and spinning reserve service. Two test VPPs are introduced and different scenarios are considered for markets prices. At first, the participation of VPP in only energy market is studied. Then, the spinning reserve market is taken into consideration and the bids of VPP in a joint market of energy and spinning reserve service is studied under different scenarios of markets prices and the results are analyzed. In all cases, the results show the effectiveness and the quality of the procedure and validate the proposed model.

Heating/cooling systems have played an important role in building energy and comfort management. There have been intensive discussions about the integration of heating/cooling systems into the smart grid infrastructure over the past decade, yet controlling the operation of heating/cooling systems in a smart grid with high penetration of renewable resources has not been addressed clearly. This study has investigated the suitability of a novel active controller applied to heating/cooling systems in the context of smart grid with high penetration of renewable energies. The proposed controller operates by responding to a combination of internal set points and external signals (e.g. the availability of renewable energy resources and welfare of customers) from a single local controller. The heating/cooling systems management minimizes the overall cost of the simulated smart microgrid, the size of smart microgrid units, and the imported energy from the distribution grid through an optimization process. It also at the same time maximizes the reliability of the smart microgrid. Demonstrated results confirm the capability of the proposed heating/cooling system controller on the planning of a smart microgrid.