Integrated Multi-criteria Decision Analysis, Sizing Optimization, and Demand Side Management for Defining Optimal System Configuration While Reducing Costs and CO2 Emissions

In response to the escalating demand for electricity driven by rapid economic development, there has been a surge in harmful gas emissions, particularly CO2, from the combustion of fossil fuels. This not only poses environmental concerns but also contributes to rising energy costs. This study aims to mitigate these issues by integrating renewable energy sources (Photovoltaic panels and Wind Turbines), Energy Storage Systems, and Electric Vehicles while employing optimization algorithms. The Energy Management Model uses Multi-Criteria Decision Analysis and Mixed-Integer Linear Programming to find optimal values for variables related to energy transactions. It considers load satisfaction and surplus energy from renewables, leading to optimal pricing and CO2 production. Sizing optimization, using Simulated Annealing and Stochastic Differential Hill Climbing through a global stochastic space search model with integrated Energy Management Model, identifies the optimal quantity and capacity of energy assets. The optimal system configuration comprises 5 Photovoltaic panels (0.4 kWh), 1 Wind Turbine (0.6 kWh), 4 kWh Energy Storage System capacity, and 14 kWh Electric Vehicle battery capacity. In contrast to a scenario relying solely on the grid, incorporating energy assets and optimization algorithms reduces the annual cost from €1430 to €1085, with CO2 production dropping from 2430 kg to 2090 kg. This reduction is accomplished, in part, through load shifting optimization, determining optimal home appliance activation hours. This comprehensive approach results in a significant 24% cost reduction and a 14% decrease in CO2 emissions, underscoring the effectiveness of integrating energy assets and optimization algorithms.