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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.

Optimal power flow (OPF), a mathematical programming problem extending power flow relationships, is one of the essential tools in the operation and control of power grids. To name but a few, the primary goals of OPF are to meet system demand at minimum production cost, minimum emission, and minimum voltage deviation. Being at the heart of power system problems for half a century, the OPF can be split into two significant categories, namely optimal active power flow (OAPF) and optimal reactive power flow (ORPF). The OPF is spontaneously a complicated non-linear and non-convex problem; however, it becomes more complex by considering different constraints and restrictions having to do with real power grids. Furthermore, power system operators in the modern-day power networks implement new limitations to the problem. Consequently, the OPF problem becomes more and more complex which can exacerbate the situation from mathematical and computational standpoints. Thus, it is crucially important to decipher the most appropriate methods to solve different types of OPF problems. Although a copious number of mathematical-based methods have been employed to handle the problem over the years, there exist some counterpoints, which prevent them from being a universal solver for different versions of the OPF problem. To address such issues, innovative alternatives, namely heuristic algorithms, have been introduced by many researchers. Inasmuch as these state-of-the-art algorithms show a significant degree of convenience in dealing with a variety of optimization problems irrespective of their complexities, they have been under the spotlight for more than a decade. This paper provides an extensive review of the latest applications of heuristic-based optimization algorithms so as to solve different versions of the OPF problem. In addition, a comprehensive review of the available methods from various dimensions is presented. Reviewing about 200 works is the most significant characteristic of this paper that adds significant value to its exhaustiveness.

An influential factor in enhancing the attendance services, mainly in commercial and emergency sectors, is the vehicular technology used to transport people, goods, or equipment. Although hybrid electric vehicles (HEVs) represent a sustainable transport alternative, the existing technical limitations such as battery and fuel capacities, and autonomy, among others, highlight the provision of an efficient automation tool. The tool can serve to enhance the operational performance of the HEV by selecting the proper driving mode (on fuel or electricity), and the navigation strategies to the delivery and charging points in urban areas. This paper proposes a two-stage methodology that allows the HEVs operators to automate the operational performance of a heterogeneous HEV fleet on a city map. Each stage is handled by its corresponding optimization model. In the first stage, the total navigation time and the battery lifetime of the fleet during the operation are optimized. In this stage, constraints related to charge-sustaining/charge-depleting modes, state of charge (SoC) of the HEVs battery, and deliveries schedules are taken into account. To this end, operating strategies related to the performance of different types of existing HEV technologies are anonymously considered. In the second stage, the best operating strategy among all the operating strategies is selected while considering the capacity of HEVs to deliver a given quantity of goods. Moreover, uncertainties during the HEV navigation are simulated considering the change in traffic density of the urban roads as a function of the levels of service (LOS). Results show that the proposed methodology establishes an efficient operational scheme for a HEVs fleet, ensuring a significant reduction of energy usage as well as mitigating the CO2 emissions.

In this paper, a mixed-integer linear programming model is proposed to optimize hybrid electric vehicle (HEV) navigation modes on the city map, namely the problem of the optimal selection of navigation modes (OSNMs). The OSNMs problem of the HEV as part of the operating strategy is obtained considering a constraint set related to CO 2 emissions reduction, efficient battery charging, and the optimal scheduling of deliveries. Uncertainties in the HEV navigation on urban roads are modeled using probability values assigned to an established set of traffic density values according to the levels of service. The model is implemented in a mathematical programming language (AMPL) and solved using the commercial solver CPLEX. The case study considers real data related to the Prius Prime technology and shows the effectiveness of automating the HEV navigation modes considering CO 2 emissions reduction levels during an operating strategy.

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.

Made available in DSpace on 2018-11-26T17:55:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-09-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) This paper presents a mixed integer linear programming model to optimize the costs of maintenance and extra hours tits scheduling a fleet of battery electric vehicles (BEVs) so that the products arc delivered to prespecified delivery points along a route. On this route, each BEV must have an efficient charging strategy at the prespecified charging points. The proposed model considers the average speed of the BEVs, the battery states of charge, and a set of deliveries allocated to each BEV. The charging points are located on urban roads and differ according to their charging rate (fast or ultra-fast). Constraints that guarantee the performance of the fleet's batteries are also taken into consideration. Uncertainties in the navigation of urban roads are modeled using the probability of delay due to the presence of traffic signals, schools, and public works. The routes and the intersections of these routes are modeled as a predefined graph. The results and the evaluation of the model, with and without considering the extra hours, show the effectiveness of this type of transport technology. The models were implemented in AMPI, and solved using the commercial solver CPLEX. Univ Estadual Paulista, Fac Engn Ilha Solteira, Dept Engn Eletr, BR-15385000 Ilha Solteira, Brazil Univ Estadual Campinas, Sch Elect & Comp Engn, Dept Syst & Energy, BR-13083852 Campinas, SP, Brazil Univ Estadual Paulista, Fac Engn Ilha Solteira, Dept Engn Eletr, BR-15385000 Ilha Solteira, Brazil CNPq: 141462/2013-2 FAPESP: 2014/22828-3 FAPESP: 2016/14319-7

Demand response (DR) programs based on pricing options allow residential customers to achieve a financial reduction in their energy bill due to changes in their consumption patterns, especially during peak periods. However, when a large number of consumers adopt this energy management program, the demand shifted to periods with low energy prices can generate new demand peaks. As a result, the quality of the power supply service may be compromised. To address this concern, this paper proposes a mixed-integer linear programming (MILP) model that aims to improve the load factor (LF) related to the demand profile of customers. To achieve this goal, an intelligent scheduling strategy for household appliances that considers flexibility in customer comfort, here called customer hourly preferences, is developed. Based on these preferences, the strategy seeks the efficient daily usage of smart appliances, mainly those with higher average power, to avoid its coincident consumption in periods with lower energy rates, thus mitigating the appearance of new peaks. In the proposed model, the operating expenses of both customers and the electricity company (ECO) are minimized. A set of technical and operational constraints such as the average power, number of times utilized, and average time of usage of home appliances, as well as the charging rate, average time for charging, and initial state-of-charge (SoC) of the plug-in electric vehicle (PEV) battery, are considered. Uncertainties related to the periods of the day when a given appliance (including PEV) is turned-on for consumption are modeled using a Monte Carlo Method (MCM). The MILP model is solved using a commercial solver CPLEX that makes use of classical optimization techniques to ensure the optimal solution to this problem. The performance of the MILP model was tested through two case studies. Case study 1 considers a group of consumers with the same income, while case study 2 triples the number of consumers in the previous case considering different incomes. The results show the importance of the proposed tool for analyzing and evaluating prospective scenarios that guarantee the efficient usage of electric energy with the lowest financial expense for both consumers and the ECO.

This paper presents a mixed integer linear programming (MILP) model to optimizing the maintenance costs during the operation of the hybrid electric vehicles (HEVs) aiming at reducing CO 2 emissions during operating along of the day. This target is obtained by an appropriate selection of navigation modes and the optimal scheduling of deliveries. The proposed model considers the average speed, battery state of charge (SOC), and the set of deliveries to be made by each type of HEV. A set of constraints that ensures the performance of the HEVs is considers while the uncertainties of the trip on each urban road are modeled using the traffic density values according to the levels of service (LOS). The proposed model is implemented in AMPL and solved using the solver CPLEX showed the effectiveness in the evaluation of each type of HEV technology.

The optimal power flow (OPF) is a key tool in the planning and operation of power systems, and aims to optimize the operational costs involved in the production and transport of energy by adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, a successful implementation of an expeditious and reliable optimization algorithm is crucial. To this end, this paper proposes and scrutinizes a novel fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution algorithms, namely FAHSPSO-DE, to address the multi-objective OPF (MOOPF) problem. For the sake of practicality, the objectives with innate differences such as total fuel cost, active power losses, and the emission are selected. Due to the practical limitations in real power systems, additional restrictions, including valve-point effect, multi-fuel characteristic, and prohibited operating zones, are also taken into account. In order to validate the performance of the proposed approach, ten various benchmark functions are examined, while three IEEE standard systems such as IEEE 30-, 57-, and 118-bus test systems are employed to demonstrate the performance and suitability of the proposed approach in solving the OPF problem expeditiously. Results have been compared with those in the literature and show the effectiveness of our proposal in handling different scales, multi-objective, and non-convex optimization problems.