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In recent years, demand-side management (DSM) strategies have become an indispensable tool in the operation and planning of modern electricity grids (EGs). One effective way of ensuring the economical and reliable operation of an EG is through assessing its load factor (LF), while considering different types of electrical equipment (e.g., residential, commercial, and industrial). Toward this end, this paper proposes a mixed-integer quadratically constrained programming (MIQCP) model to deal with the LF assessment problems in a modified power distribution system through optimal scheduling of electrical equipment. This MIQCP model aims to minimize the total costs of purchasing energy by the electric utility via an iterative process, in which the difference between the energy consumption in each period and the average consumption is reduced. In the proposed model, the uncertainties in the consumption habits of different consumers, information related to each electrical equipment, energy prices, and the grid’s technical constraints are considered. A modified 34-node EG, differentiated by consumer type, is implemented to evaluate the proposed model. Results show that the LF value is related to the optimal scheme of the electrical equipment that meets the operational and economic requirements of the power grid.

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.

Funding Information: Funding: The APC was funded by Aalto University. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. In prosumers’ communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large‐scale use of home appliances in periods of lower solar radiation and low electricity tariff can impair the performance of the electrical system. The appearance of new consumption peaks can lead to disturbances. Moreover, the repetition of these events in the short term can cause rapid fatigue of the assets. To address these concerns, this research proposes a mixed‐integer linear programming (MILP) model aiming at the optimal operation of the SBs and the appliance usage of each prosumer, as well as a PV plant within a community to achieve the maximum load factor (LF) increase. Constraints related to the household appliances, including the electric vehicle (EV), shared PV plant, and the SBs, are considered. Uncertainties in consumption habits are simulated using a Monte Carlo algorithm. The proposed model was solved using the CPLEX solver. The effectiveness of our proposed model is evaluated with/without the LF improvement. Results corroborate the efficient performance of the proposed tool. Financial benefits are obtained for both prosumers and the energy company.

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.

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.