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Publisher Copyright: © 2022 Elsevier Ltd In Smart Cities (SC), the efficient management of services such as health, transport, public safety, and especially the electricity ensures the welfare of citizens. In recent years, the insertion of renewable sources (RSs) (e.g., solar and wind) in the power grid (PG) of SCs has contributed to meeting the electricity needs of the various consumer units. However, the large-scale integration of these RSs can fatigue the assets, leading to their premature aging and, consequently, compromising the quality of electricity supply. To overcome these challenges, the implementation of Neighboring Energy Storage Communities (NESCs) employing demand response (DR) strategies along with efficient coordination of storage batteries (SBs) could be a promising alternative. In this sense, the present work proposes a mixed-integer linear programming (MILP) model to efficiently manage SBs and the set of household appliances, including charging electric vehicles (EVs), in an NESC provided solely by PG. The proposed model aims to minimize: the total costs related to energy consumption, the peak rebound effect on the total consumption profile, energy wastage through load factor (LF) improvement, and the deep discharges in the SBs during their daily operational cycle. Operational constraints related to the home appliances, such as average usage time, the number of times that the appliance is used daily, etc., are taking into account. The EV state-of-charge (SOC), EV charging rate limits, and initial and final SOC of the SBs, are also considered. A Monte Carlo Algorithm (MCA) is used to simulate the habitual consumption patterns of each customer. The proposed model was implemented in AMPL and solved using CPLEX. The performance of this proposed model is evaluated considering two NESCs differentiated by the number of consumer communities. A first NESC (small-scale) is analyzed considering only two consumer communities. In this NESC, two case studies (Case 1 and 2) are discussed. Next, the second NESC (large-scale) that considers 14 consumer communities is analyzed for the most complete case study (Case 2). Within each NESC, consumer communities are differentiated by the household income and the types of SBs (individual and shared) that support each community. The results corroborate the applicability of the MILP model to real case studies on a diverse scale, guaranteeing the efficient use of PG at the same time that each SB seeks the most optimized operation.

Funding Information: This work was supported by the National Natural Science Foundation of China (Grant Nos. 52171292, 51939001), Dalian Outstanding Young Talents Project (Grant No. 2022RJ05). Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents an optimal active power dispatch (OAPD) problem that, unlike common economic dispatch problems, precludes unwanted mismatches on realistic power systems. The OAPD is formulated by considering the unified power flow controller (UPFC), a versatile device from the flexible AC transmission systems. However, the resultant turns into a highly nonlinear and complex optimization problem, which requires a powerful evolutionary algorithm to determine the optimal solutions. Toward this end, this paper explores the use of comprehensive learning particle swarm optimization and differential evolution as a hybrid configuration in a fuzzy framework, called hybrid fuzzy-based improved comprehensive learning particle swarm optimization-differential evolution, to address the proposed problem. To demonstrate the performance of the proposed algorithm, a set of benchmark problems, including real-world constrained optimization problems as well as a profound analysis of Schwefel problem 2.26 are provided. Moreover, to authenticate its effectiveness in solving power and energy-related problems with quite a few decision variables, four different power systems, 3-unit, 6-unit IEEE 30-bus, 10-unit, and 40-unit systems, are implemented. The IEEE 30-bus system is opted for profoundly analyzing the performance of the proposed algorithm in handling the optimal power dispatch problem considering security constraints and UPFC device, where an enhancement, at least $74,000 saving in a 365-day horizon, in total generation cost is obtained. Simulation results also validate that evolutionary algorithms need to be improved/hybridized to achieve better equilibrium between exploration and exploitation processes in a timely manner while solving power and energy-related problems.

In residential units (RUs), utilizing a grid-connected photovoltaic (PV) source allows reducing the energy bill, as well as guarantee a continuous supply. However, the large-scale injection of PV energy surplus may cause congestion in the electrical network (EN), especially in the feeders, compromising its safety and reliability. To address this concern, this work proposes a mixed-integer linear programming (MILP) model for the intelligent management of energy in an RU with the support of a hybrid PV scheme (HPVS). The proposed model aims to minimize the energy consumption costs due to the optimal scheduling of residential appliances usage, including the electric vehicle (EV) battery charging. The proposed HPVS makes efficient use of a PV-array, storage battery (SB), and the energy provided by EN in order to meet the consumption needs of the RU. Operating constraints and technical limits related to the performance of each technology are considered. The simulation of uncertainties in the home appliances usage, EV charging, as well as the levels of solar radiation due to weather variation, are performed to obtain a more realistic MILP model. The results show the efficient performance of the proposed HPVS scheme in reducing RU energy consumption costs, as well as in optimizing the lifetime of the equipment, such as SB, PV array, EN’s domestic distribution network, without compromising the continuity of the supply service.