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Multi-agent based small scaled smart grid reinforcement scheme is proposed to manage energy resources by enhancing resilience to supply power to critical loads in peak demand by leveraging demand side management (DSM) for smoothing load profile and optimal energy storage system (ESS) scheduling in response to grid cost. Interconnected microgrids comprise diverse energy resources that allow sharing energy to balance fluctuation by deploying a market that rewards cheap energy. Three stages are defined in this proposed scheme. In the first stage, without DER, consumption charges are reduced to 7.1% through DSM, but customer comfort is compromised. In the second stage, microgrids comprise RER; energy consumption-related charges are reduced to 92.2% by the feed-in tariff (FIT) Program. In the final stage, the inclusion of energy market for trading among microgrids (ESS, diesel and RER) resulting in reduced charges greater than charges incurred from the grid is defined by 122.1%. The findings show that the average power drawn from utility grid increased from 134.7kW to 205.3kW, and peak demand decreased from 591kW to 495kW. Integer linear programming optimization technique is applied to allow the optimal dispatch of microgrids generation to reduce the operation cost to 42.64%. Less reliance on the grid, flexible energy use, and avoiding costly electricity purchases are achieved through DSM, effective resources scheduling, and energy trading.

Distributed generation has enhanced power production in recent times. Due to their benefits, Ghana is interested in grid-tied solar photovoltaic (PV) systems. Despite the benefits, solar PV integration studies in Ghana have not advanced. This study examines reverse power flow (RPF) due to solar PV in Low Voltage (LV) network branches. The methodology uses a modified IEEE European test network and an Electricity Company of Ghana (ECG) LV network. ETAP software is used to simulate the two solar PV integrated LV networks, and the obtained data is used to formulate correlation models of solar PV penetration and key network parameters in Excel. Model results estimate the RPF critical values for the modified IEEE European test network and the ECG LV network as 7.36 kW and 7.44 kW, respectively. The RPF values are obtained at maximum penetration depths of 62.6% and 69.8% respectively. At maximum penetration levels, predicted line loadings are 6.42% and 7.28% respectively. Further analysis reveals branch-transformer RPF margins of 26.8% and 23.1% in the modified IEEE European test network and the ECG LV network respectively. The results are essential for establishing pre-determined settings to safeguard LV network branches and transformers from overload due to RPF.

This paper presents an Equilibrium Optimizer (EO)-based method that simultaneously determines locations and sizes of multiple photovoltaic (PV) units for maximizing a distribution system’s PV hosting capacity (PVHC). The proposed method avoids various search-space restrictions seen in recent literature. The constraints in this paper are separated into two types based on the necessity of power flow analysis. The proposed method is implemented through co-simulation between MATLAB and OpenDSS. PV output and load demand variation is considered through a 24-hour profile representing a season. Smart inverter is considered through the PV inverter volt-var control (VVC) scheme. The proposed method is tested on the IEEE 123-bus benchmark system. Four test cases are considered to determine the impact of reverse power flow (RPF) limit and the impact of VVC toward the optimal PV planning. The results show that EO produces superior results compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Coyote Optimization Algorithm (COA).

The use of electric vehicles (EVs) and photovoltaics (PV) is increasing worldwide. Transportation networks require the effective use of renewable energy (RE) for EVs, whereas power networks require local consumption of PV energy, mainly at the initiative of local governments. Although many previous studies have addressed these requirements using private EVs as mobile storages, their uncertainty and uncontrollability remain highly problematic. Therefore, our previous studies focused on electric buses because of their high controllability and certainty. These studies involved the development and evaluation of two independent minimization problems—kilowatts (KW) and kilowatt-hours (KWH) of surplus RE—as a charging schedule optimization method using mixed integer linear programming. However, the feasibility of simultaneously minimizing KW and KWH still presented technical problems. This study aims to extend and generalize the method to the simultaneous minimization of KW and KWH of the PV-derived reverse power flow (RPF). With this multiobjective optimization, the KW peak-cut of the RPF improves the hosting capacity and increases the availability of connectable RE resources, whereas the minimization of KWH promotes the local consumption of RE and decarbonization of public transportation. Two simulations using detailed data of actual bus operations and actual power flow data for a real city confirmed the feasibility of simultaneously optimizing KW and KWH and the relationship between these indicators and number of EV chargers. The optimized charging of the 17 electric buses achieved a maximum of 211.4 kW peak-cut and 1318.4 kWh RE-RPF absorption, reducing CO2 emissions by 495.7 kg/day.