• Energy Research

The virtual energy storage system (VESS) is one of the emerging novel concepts among current energy storage systems (ESSs) due to the high effectiveness and reliability. In fact, VESS could store surplus energy and inject the energy during the shortages, at high power with larger capacities, compared to the conventional ESSs in smart grids. This study investigates the optimal operation of a multi-carrier VESS, including batteries, thermal energy storage (TES) systems, power to hydrogen (P2H) and hydrogen to power (H2P) technologies in hydrogen storage systems (HSS), and electric vehicles (EVs) in dynamic ESS. Further, demand response program (DRP) for electrical and thermal loads has been considered as a tool of VESS due to the similar behavior of physical ESS. In the market, three participants have considered such as electrical, thermal and hydrogen markets. In addition, the price uncertainties were calculated by means of scenarios as in stochastic programming, while the optimization process and the operational constraints were considered to calculate the operational costs in different ESSs. However, congestion in the power systems is often occurred due to the extreme load increments. Hence, this study proposes a bi-level formulation system, where independent system operators (ISO) manage the congestion in the upper level, while VESS operators deal with the financial goals in the lower level. Moreover, four case studies have considered to observe the effectiveness of each storage system and the simulation was modeled in the IEEE 33-bus system with CPLEX in GAMS.

Over the past decades, significant revolutions have occurred on electricity market to reduce the electricity cost and increase profits. In particular, the novel structures facilitate the electricity manufacturers to participate in the market and earn more profit by cooperate with other producers. This paper presents a three-level gameplay-based intelligent structure to evaluate individual and collaborative strategies of electricity manufacturers, considering network and physical constraints. At the Level , the particle swarm optimization (PSO) algorithm is implemented to determine the optimum power of distributed energy resources (DERs) in the power grid, to maximize the profits. Further, the fuzzy logic algorithm is applied to model the intermittent nature of the renewable sources and implement load demand in the power grid. At the Level , DERs are classified into two different fuzzy logic groups to secure the fairness between every participant. Finally, at the Level , the DERs in each group are combined each other by cooperative game theory-based algorithms to increase the coalition profits. Thereafter, Shapley, Nucleolus, and merge/split methods are applied to allocate a fair profit allocation by coalition formation. Ultimately, the results verify the proposed model influence electric players to find effective collaborative strategies under different conditions and environments.

Integration of distributed energy resources (DER) in electrical microgrids introduces residential green buildings (RGB) with the promising decrement in fossil fuel consumption. This novel concept compromises with numerous challenges such as control DERs and consumers in a home microgrid (H-MG), based on historical data and market clearing price (MCP), which requires multi-objective analysis and energy management system (EMS). Identically, tremendous issues emerged when integrating these systems with RGBs. Therefore, multi-agent systems (MAS) are capable of utilizing parallel computing as a control method, where each RGB is represented as an agent with independent decision-making capability, while productively cooperating with other agents. In this paper, an effective EMS has been presented with MAS (EMS-MAS) for DER in a neighbourhood grid, accompanied by several RGBs. The RGB includes controllable and uncontrollable devices and several electrical and thermal loads along with the retailers. Finally, the results confirm that proposed model has significantly enhanced the overall energy efficiency and the profit of individual RGBs, and optimally managed the devices in RGBs while encouraging demand response (DR) load programs, retailers and MCP reduction.