Assessing the economic and energy efficiency for multi-energy virtual power plants in regulated markets: A case study in Egypt

Abstract

• Multiple configurations of VPP aggregating CCHP and Solar PV are assessed. • New profit maximization objective function is presented for regulated markets • Optimization is solved with GA simulating different system sizes. • The propsoed method improved the cost of energy and reduced grid dependency This paper investigates the design and operation management of VPPs in regulated markets. A new framework based on profit maximization objective function is presented in this study. The hypotheses of this research is that considering profit as an objective function would yield a more realistic and optimal sizes compared to Cost of Energy (COE) minimization approach adopted in literature. The analyzed VPP aggregates solar PV units, CCHP supplying power and thermal energy, Battery storage system and thermal energy storage system. The system is formulated in an optimization model fed by energy demand profile, prices and inputs for solar power (irradiance and weather data). The objective function is formulated based on maximization of profit of the VPP selling power to the grid by Power Purchase Agreement (PPA), selling power to consumers at the public electricity tariff, and selling thermal energy at an assumed constant tariff. CCHP non-linear part-load efficiency is also considered in the model, accordingly, Genetic Algorithm (GA) is employed to solve the optimization. Results of the optimally configured model achieved 36% improvement in COE compared to literature. Solar power contributed by 31% from the total produced energy without imbalance, grid power contributed by 4%, and CO2 emissions reduced by 47% compared to full dependency on the grid. Statistical relationships were drawn showing the relationship between profit, energy and exergy efficiencies versus different CCHP capacities. In addition, analysis is provided for the efficiencies’ relation with the dumped heat from the CCHP.