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A PSO based optimal EVs Charging utilizing BESSs and PVs in buildings
Abstract
The penetration of Electric Vehicles (EVs) is rapidly increasing at the Low Voltage (LV) level, thus increasing the electrification rate of transport. Moreover, Photovoltaic (PV) systems at the LV level have been constantly increasing as well. Buildings with PVs on their roof and EVs as the main transport means may well become the norm the following years, considering additionally that all new buildings in the EU should be Nearly Zero Energy Buildings (NZEBs). Such NZEBs should cover the majority of their low energy demand by Renewable Energy Sources, and PV technology is the main candidate to achieve that. Both these facts pose considerable challenges on the LV networks. In this context, this paper examines the potential of efficiently utilizing the stored energy of Battery Energy Storage Systems (BESSs) in order to face the increased load demand by the penetration of EVs in LV Distribution Networks (DNs), with the presence of PVs. A Particle Swarm Optimization (PSO) algorithm is developed in order to perform optimal charging schedule for the EVs under the aim of optimizing the DN voltage profile. The analysis is performed on a real LV DN with real load data and the results indicate that under proper charging schedule both the voltage profile and the energy losses of the DN could be improved.
... The aim is to impose load shifting and regulate the demand profile. Similarly, in [17] the Particle Swarm Optimization (PSO) is employed to mitigate voltage violations. Further on, the authors in [18] utilize a hybrid Weight Aggregation-Multi Objective PSO strategy to mitigate power fluctuations and minimize charging costs. ...
... The proposed methodology is applied on a real rural LV DN consisting of 108 residences [17]. The DN is radial with four basic feeders and several laterals as presented in Fig.3. ...
This paper deals with the problem of energy losses in Distribution Networks (DNs) under Electric Vehicle (EV) penetration. The problem of charging overlaps causing severe power losses and voltage drops is faced under appropriate EV charging. An optimization EV charging schedule is proposed under EV time-of-arrival consideration. The solution algorithm is based on a Particle Swarm Optimization (PSO) variant and both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) schemes are included regarding the power interactions of the EVs with the grid. The goal is to optimally allocate the charging and/or discharging time periods of the EVs in order to minimize energy losses while delivering fully charges EVs to the owners. New departures after the first EVs’ arrival are also handled. The results indicate that with the proposed scheduling, daily energy loss reduction could be reached up to 25% under an improved voltage profile that indicates more uniform loading for the DN.
... In [12], the application of battery energy storage systems (BESSs) was studied in order to face the increased load demand by the penetration of EVs in distribution networks considering photovoltaic units. A Particle Swarm Optimization (PSO) algorithm was developed in order to perform optimal charging schedule for the EVs under the aim of optimizing the distribution network voltage profile. ...
Over the last decade, the electrification of the transportation fleet emerged as a solution to reduce climate change. To electric vehicles (EVs) become widespread, charging stations must be deployed, especially fast stations (FCSs), to allow over-ranged travel. Previous works have analyzed the technical impacts of FCSs, also in combination with photovoltaic (PV) and battery energy storage system (BESS); however, a combined stochastic technical–economic evaluation has been less discussed. The objective of this work is to develop a technical–economic method to determine: (i) the most profitable time-of-use electricity tariff for a charging station; and (ii) the economic feasibility of PVs and BESS integration with FCSs and how these technologies affect the overall profitability. A real Brazilian distribution system is used as a case study, considering local time-of-use tariffs for low (LV) and medium (MV) voltage customers. Technical results show that PVs and BESSs can reduce 4–7% voltage problems; however, may increase overload issues, especially in LV connection. From the economic perspective, MV connections have higher profits using Blue tariff instead of Green, while LV connections are preferred with the Conventional tariff. Also, PVs are cost-effective equipment to be added to the charging station, while storages are only profitable if they only use the PV energy generated to supply the EVs, i.e., a low daily number of EVs charging in the FCS.
Optimal charge scheduling of electric vehicles in solar-powered charging stations based on day-ahead forecasting of solar power generation is proposed in this paper. The proposed algorithm’s major objective is to schedule EV charging based on the availability of solar PV power to minimize the total charging costs. The efficacy of the proposed algorithm is validated for a small-scale system with a capacity of 3.45 kW and a single charging point, and the annual cost analysis is carried out by modelling a 65 kWp solar-powered EV charging station The reliability and cost saving of the proposed optimal scheduling algorithm along with the integration and the solar PV system is validated for a charging station with a 65 kW solar PV system having charging points with different charging powers. A comprehensive comparison of uncontrolled charging, optimal charging without solar PV system, and optimal charging with solar PV system for different vehicles and different time slots are presented and discussed. From the results, it can be realized that the proposed charging algorithm reduces the overall charging cost from 10–20% without a PV system, and while integrating a solar PV system with the proposed charging method, a cost saving of 50–100% can be achieved. Based on the selected location, system size, and charging points, it is realized that the annual charging cost under an uncontrolled approach is AUS $28,131. On the other hand, vehicle charging becomes completely sustainable with net-zero energy consumption from the grid and net annual revenue of AUS $28,134.445 can be generated by the operator. New South Wales (NSW), Australia is selected as the location for the study. For the analysis Time-Of-Use pricing (ToUP) scheme and solar feed-in tariff of New South Wales (NSW), Australia is adopted, and the daily power generation of the PV system is computed using the real-time data on an hourly basis for the selected location. The power forecasting is carried out using an ANN-based forecast model and is developed using MATLAB and trained using the Levenberg–Marquardt algorithm. Overall, a prediction accuracy of 99.61% was achieved using the selected algorithm.
The escalating demand on electric vehicles (EVs) has enhanced the necessity of adequate charging infrastructure, especially in residential areas. This article proposes a smart charging approach for off-board EVs chargers in home-energy-hub (HEH) applications along with dc sources such as photovoltaic and battery storage (BS). The proposed method facilitates smart charging and discharging of EVs to obtain both vehicle-to-x and x-to-vehicle operations focusing on the domestic applications integrated with renewable and storage elements. Furthermore, the optimal state-of-charge (SOC) profiles for BS and EV in the HEHs system is defined by the extended Bellman-Ford-Moor algorithm (BFMA). This modified BFMA utilizes the forecasted data such as solar irradiation, electricity tariff, and power consumption to gain economic benefits in HEHs with respect to the user and EV requirements. Moreover, the plugging time, duration, and initial/final SOC are fluctuating at each connection due to the stochastic nature of EV conditions and user settings. This study presents a laboratory implementation of two-level hierarchical energy management system for HEHs with plug-in electric vehicles. In fact, the primary level includes power converters controller, while the proposed algorithm is implemented in the secondary level. Finally, the simulation and experimental results confirm the effectiveness of the proposed analysis regarding the interaction of HEHs and power grid with EVs behavior.
Being an engineering field, power systems provide an extensive subject for optimization to be applied upon. Modern power systems have evolved in an increasingly highly complex system. The liberalization of the energy market and the introduction of distributed generation and, in particular, distributed renewable energy resources (DRES) have raised both opportunities and challenges that need to be tackled. Thus, complex issues related to the operation and planning of the distribution systems have emerged. Such issues involve many variables and refer to nonlinear objectives; thus their optimization is significantly based on heuristic techniques, such as particle swarm optimization (PSO). In this chapter, the implementation of PSO when contemplating various problems in power systems is presented. In particular, the utilization of PSO is demonstrated in the optimal distributed generation placement problem (ODGP), also known as optimal siting and sizing of distributed generation problem, and in the network reconfiguration problem. Finally, PSO is implemented in an optimal schedule of electric vehicles (EVs) charging, providing an apt example of the variety of problems for which PSO can be utilized and providing useful aid to important decisions, in the field of power systems.
An increase in the penetration of renewable energy sources in the electrical production has been matched by the emergence of many and varied challenges and problems. Among the most important challenges is finding the smart technologies and algorithms which are capable of achieving efficient solutions. This chapter provides an expanded view of the uses of the particle swarm optimization (PSO) algorithm in the renewable energy systems field. Additionally, it describes how the algorithm can be developed to cope with problems related to renewable energies to achieve desired goals. The PSO algorithm was used to solve many problems in the renewable energy systems, such as in optimal hybrid power systems, optimal sizing, and optimal net present cost, among others, where the PSO algorithm showed its high adaptability in problem-solving. Further, many researchers proceeded with the study and development of the PSO algorithm. In contrast, other researchers tried to hybridize it with different algorithms to be more efficient and convenient to overcome some of the problems and challenges that they encountered. The renewable energy systems have several issues to discuss, such as the cost of investment, the feasible technical criteria, optimal control, and the ecological problems as well as the social effect. Overall, studies and research have proven that the PSO algorithm is one of the best algorithms used in the field of renewable energy. This is attributed to the algorithm’s simplicity, high efficiency, and effectiveness compared to other algorithms and optimization methods.
Decentralized energy storage systems (ESSs) can provide great advantages to low-voltage (LV) distribution networks, enhancing the further penetration of photovoltaic (PV) systems in existing electrical grids. An ESS can locally absorb the PV produced energy during periods of high solar irradiation, thus alleviating potential overvoltages in distribution system, caused by reverse power flow. This work investigates the impact of battery ESSs operation when installed in residential PV prosumers. The charging process of the battery is activated when PV power exceeds a certain power threshold, mitigating the generation peaks in the grid. A sizing method is developed to estimate the capacity of ESSs, aiming to maximize prosumer self-consumption ratio (SCR). The proposed methodology is tested in a real LV network with increased PV integration. Results verify the effectiveness of the developed method. Overvoltages are successfully avoided while prosumers' SCR is maximized and storage utilization is preserved high. The methodology may constitute a useful tool for both distribution system operators and prosumers having storage systems.
It is expected that the number of electric vehicles will increase significantly worldwide. Although, this increase represents an environmental advantage. Charging of plug-in electric vehicles (PEVs) considers a challenge for the electric utilities. In this paper, the charging impact of PEVs on a benchmark low voltage (LV) grid has been studied. The LV network used as a case study is developed by the EU project “Microgrids”. Two charging scenarios have been investigated in this paper, 1) uncontrolled charging, and 2) Indirect controlled charging with Time of Use (TOU) pricing. In each scenario, different penetration levels of PEVs have been studied (e.g., 20 %, 30%, and 50%). The effects of PEVs charging on transformer loading, feeders loading, and voltages at the endpoints of the feeders have been investigated. 24-hour time-series simulations have been used in the study. The results demonstrate that with the use of indirect controlled charging a higher number of PEVs can be charged from the distribution network without infrastructure upgrade and for uncontrolled charging infrastructure upgrade will be needed.
The paper presents a new bi-level optimization framework for optimal accommodation and operational management of wind power generation and Battery Energy Storage System (BESS) simultaneously, aiming to maximize the renewable hosting capacity of distribution networks. A new objective function is suggested comprising of annual energy loss in feeders, reverse power flow into the grid, non-utilized BESS capacities, roundtrip conversion losses of BESSs and node voltage deviation subjected to various system security constraints.
An artificial-intelligence based optimal management of BESS is proposed for effective control of high renewable power generation. Due to the high investment and running costs of BESS, minimum storage capacity has been ensured in planning stage. In order to show the effectiveness of the proposed model, it is implemented on a benchmark test distribution system of 33-bus. Besides, various test cases are investigated and compared, which shows that the proposed optimization model is promising.
In this paper, a comparative analysis and evaluation of several heuristic techniques, when applied to the Optimal Distributed Generation Placement (ODGP) problem, is presented. Loss minimization is considered as the objective, while the network's technical characteristics as the constraints. Three versions of Particle Swarm Optimization (PSO), Global, Local and Unified (GPSO, LPSO and UPSO, respectively), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS) and Harmony Search (HS) are compared. The implemented analysis demonstrates that all Heuristic Techniques examined can solve the ODGP problem efficiently, although via UPSO an enhanced performance is achieved, both in terms of a better optimal solution as well as faster convergence, while HS proves the fastest. The study is evaluated upon typical 33 and 30 bus systems.
Nowadays, one of the dominant reasons of excessive energy consumption is the high energy demand in corporate and/or public buildings. At the same time, electric vehicles (EVs) are becoming more and more popular worldwide being a considerable alternative power source when parked. In this work we initially propose an energy management framework which optimizes the control of the charging-discharging schedule of a fleet of EVs arriving at a university building for two typical load-days in February and May aiming at the minimization of the energy demand and, thus, the electricity cost of the building. To this end, a mixed integer linear programing (MILP) model containing binary and continuous variables was developed. Uncertainties in load, generation, and cost require modeling power systems with a probabilistic approach. In such a way, the probabilistic nature of demand side management (DSM) problem is also possible to be addressed. The integration of the EVs in the Low Voltage (LV) grid is simulated with a probabilistic analysis framework that uses real smart metering (SM) data. The stochastic character of the loading parameters at the network nodes is studied taking into account the charging energy needs of the corresponding EVs fleet.
In this work, a new methodology based on a mixed integer linear programming formulation is proposed to solve the optimal charging coordination of electric vehicles (EVs) in unbalanced electrical distribution systems (EDS) considering vehicle-to-grid (V2G) technology. The steady-state operation of the EDS is represented using the real and imaginary parts of voltages and currents at nodes and circuits respectively. Distributed generation (DG) and the imbalance of the system circuits and loads are taken into account. The developed method defines an optimal charging schedule for the EVs. This charging schedule considers the EVs’ arrival and departure times and their arrival state of charge, along with the energy contribution of EVs equipped with V2G technology. The presented formulation was tested in a 123-node distribution system. The charging schedule obtained was compared in terms of V2G and DG scenarios, demonstrating the efficiency of the proposed method.
Integration of renewable energy sources in distribution network has increased, in order to reduce power losses in the network. Electric vehicle can complement the stochastic nature of renewable sources. Electric vehicle batteries can store power while the excess amount of power available in network. The primary idea is to have optimal power losses for the residential distribution network. It can be achieved by managing the Electric vehicles charging in the network. The objective of this paper is to focus on reducing power losses in the systems as well as maintaining the voltage profile of the network Optimization method is applied to generate EV battery placement strategies on a real distribution system. Voltage and current levels are monitored on DIgSILENT power factory. Power losses are optimized in the MATLAB optimization toolbox (Optimitool).
MATPOWER is an open-source Matlab-based power system simulation package that provides a high-level set of power flow, optimal power flow (OPF), and other tools targeted toward researchers, educators, and students. The OPF architecture is designed to be extensible, making it easy to add user-defined variables, costs, and constraints to the standard OPF problem. This paper presents the details of the network modeling and problem formulations used by MATPOWER, including its extensible OPF architecture. This structure is used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits. Simulation results are presented for a number of test cases comparing the performance of several available OPF solvers and demonstrating MATPOWER's ability to solve large-scale AC and DC OPF problems.
This paper studies the electric vehicle (EV) charging scheduling problem of a park-and-charge system with the objective to minimize the EV battery charging degradation cost while satisfying the battery charging characteristic. First, we design the operating model of the system while taking the interests of both customers and parking garage into consideration. Subsequently, a battery degradation cost model is devised to capture the characteristic of battery performance degradation during the charging process. Taking into account the developed battery degradation cost model, EV charging scheduling problem is explored and a cost minimization problem is formulated. To make the problem tractable, we investigate the features of the problem and decompose the problem into two sub-problems. A vacant charging resource allocation algorithm and a dynamic power adjustment algorithm are proposed to obtain the optimal solution of cost minimization. Several simulations based on realistic EV charging settings are conducted to evaluate the effectiveness and applicability of the proposed methods in discussed charging scenarios. Simulation results exhibit the superior performance of the proposed algorithms in achieving the most degradation cost reduction and the lowest peak power load compared with other benchmark solutions, which is beneficial for both customers and charging operators.
The increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an OPF problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed MPC-based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulation on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.
This paper proposes a strategy for optimal integration of battery energy storage systems (BESSs) to improve the load and distributed generation (DG) hosting ability of the utility grid. An effective tool that determines the optimal capacity and day-ahead operation strategy for deployment of distribution network operator (DNO)-controlled BESSs is presented. It is a cost-based multiobjective optimization strategy that considers two primary factors: 1)distribution system cost; and 2) battery cycling cost. Quantitative analyses on the benefits and tradeoffs of BESS installations are carried out considering different service options. BESS is investigated for three main service options: 1) voltage regulation; 2) loss reduction; and 3) peak reduction. The performance and benefits of the optimized BESS to control one service option exclusively or multiple services simultaneously is compared. The analysis is further extended to study the effect of installation site on the size, management strategy, and the service option. Results show that optimal integration of BESSs can realize maximum operational and cost benefits while effectively elevating the load and DG hosting capability of the network. The approach is developed using MATLAB interior-point algorithm. Simulations are conducted for the medium voltage (MV) IEEE 33 bus system and a low voltage (LV) distribution network in Western Australia studied during the Perth Solar City Trial.
The optimization of nonlinear functions using particle swarm
methodology is described. Implementations of two paradigms are discussed
and compared, including a recently developed locally oriented paradigm.
Benchmark testing of both paradigms is described, and applications,
including neural network training and robot task learning, are proposed.
Relationships between particle swarm optimization and both artificial
life and evolutionary computation are reviewed
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