Browsing by Author "Naderi, Ehsan"

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Intelligent energy management in a prosumer community considering the load factor enhancement
(2021-06-02) Cerna, Fernando V.; Pourakbari‐kasmaei, Mahdi; Pinheiro, Luizalba S.S.; Naderi, Ehsan; Lehtonen, Matti; Contreras, Javier
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Load Factor Assessment of the Electric Grid by the Optimal Scheduling of Electrical Equipment-A MIQCP Model
(2021) Cerna, Fernando V.; Pourakbari-Kasmaei, Mahdi; Naderi, Ehsan; Lehtonen, Matti; Contreras, Javier
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
A novel hybrid self-adaptive heuristic algorithm to handle single- and multi-objective optimal power flow problems
(2021-02) Naderi, Ehsan; Pourakbari-Kasmaei, Mahdi; Cerna, Fernando V.; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Optimal operating scheme of neighborhood energy storage communities to improve power grid performance in smart cities
(2023-02-01) Cerna, Fernando V.; Pourakbari-Kasmaei, Mahdi; Barros, Raone G.; Naderi, Ehsan; Lehtonen, Matti; Contreras, Javier
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Optimization of active power dispatch considering unified power flow controller: application of evolutionary algorithms in a fuzzy framework
(2023-01-31) Naderi, Ehsan; Mirzaei, Lida; Pourakbari-Kasmaei, Mahdi; Cerna, Fernando V.; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
State-of-the-art of optimal active and reactive power flow: A comprehensive review from various standpoints
(2021-07-29) Naderi, Ehsan; Narimani, Hossein; Pourakbari-Kasmaei, Mahdi; Cerna, Fernando V.; Marzband, Mousa; Lehtonen, Matti
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
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.
Transmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approach
(2020-02-01) Naderi, Ehsan; Pourakbari-Kasmaei, Mahdi; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This paper develops a novel hybrid algorithm for solving transmission expansion planning (TEP) problems in electric power networks. Raising the awareness about immense contaminants produced by fossil fuels as well as depleting these resources have pushed energy companies toward considering more renewable energy resources (RERs). The RESs are beneficial for the society and the power system utility, however, taking into account the uncertainties, which are inherent in RERs, increase the complexity of the optimization problems. In this work, a Monte-Carlo simulation (MCS) is used to address the intermittent nature of wind energy. To handle the resulted model, by modifying and combining three well-known evolutionary algorithms such as shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), a potent hybrid MSFLA-MPSO-MTLBO, namely combinatorial heuristic-based profound-search algorithm (CHPSA), is proposed. A self-adaptive probabilistic mutation operator (SAPMO) is employed to enhance the effectiveness and computational efficiency of the CHPSA. Ten commonly-used benchmark problems are introduced to corroborate the performance of the CHPSA, while the IEEE RTS 24-bus test system is used to validate the model. Results show that the proposed CHPSA is capable of obtaining better solutions than other algorithms, either implemented in this paper or borrowed from the literature.