Distributed Energy Storage Devices Research Articles

Electric vehicle participation for optimized load frequency control in a multi-area restructured power system

ABSTRACT Electric vehicles (EVs) can be employed intelligently in Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) modes for Load Frequency Control (LFC) in the power grid. Due to the rapid variation of charging or discharging power, EVs can perform frequency regulation tasks and serve as distributed energy storage devices. In this paper, a linearized model of an interconnected multi-area power system is formulated in the restructured environment to include Electric Vehicle Aggregators (EVA). The DISCO Participation Matrix (DPM) is used to model Poolco and bilateral contracts in restructured multi-area power systems along with the participation of EVs. The faster dynamic response of EVs can effectively control the deviations in area frequencies imposed by the DISCOs load demand. The gains of the integral controller employed in each area are tuned by Real Coded Genetic Algorithm (RGA) by minimizing the integral square error function for area frequency deviations. The efficacy of power support from the EVs for load frequency control is validated by simulating different Poolco and bilateral transactions. It is shown that the EVs have a significant impact on obtaining optimal transient response for area frequencies, tie-line powers and power generation of conventional thermal generating units for any type of transaction in a restructured environment.

Optimal configuration and power scheduling algorithm of distributed power system with large range power flow transfer

The distributed power system is composed of multiple distributed energy sources, loads and energy storage devices, which have the characteristics of decentralization and complexity. Therefore, a distributed power system optimal allocation and power dispatching algorithm considering large-scale power flow transfer is proposed. Calculate the power flow values of different nodes, and construct an optimization model according to the results to complete power flow transmission. In the inner loop, the current dispatching is set as the objective, and in the outer loop, the voltage dispatching is set as the objective. Distributed power system scheduling is completed by double closed-loop vectors. The experimental results show that the power oscillation amplitude of this method is small and the duration is short.

CASL: A Novel Collusion Attack Against Distributed Energy Management Systems

Recently, the security issues of smart grids have received wide attention. In particular, false data injection attacks against distributed energy management systems (DEMSs) are considered of high importance as they are able to cause economic losses or even damages to system stability in subtle ways. Existing studies have identified many such attacks and proposed corresponding countermeasures. However, in this paper, we show that DEMS is still insecure and in risk of economic loss by proposing and analyzing a novel attack called Collusion Attack between a Storage and a Load (CASL). In CASL, a distributed energy storage device colludes with a load by injecting an abnormal extra power supply to the load in a secret way through using two secret functions. In this way, the colluding pair behaves as normal ones in both computational and communicational viewpoints. We theoretically prove the convergence of DEMS in presence of the attack, and derive an upper bound of the social welfare losses of DEMS caused by CASL. We demonstrate the effectiveness of CASL through simulations.

Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation

More and more distributed generation (DG) and energy storage (ES) devices are being connected to the distribution network (DN). They have the potential of maintaining a stable supply load during failure periods when using islanding operations. Therefore, DG and ES have capacity value, i.e., improving the power supply capability of the system. However, there are strong fluctuations in DG outputs, and the operations of ES devices have sequential characteristics. The same capacity of DG has different load-bearing capabilities compared to conventional thermal or hydroelectric units. This paper proposes a method for evaluation of power supply capability improvement in DNs. First, the temporal fluctuation in both power source and load demand during fault periods is considered. A DN island partition model considering the secondary power outage constraint is established. Then, a modified genetic algorithm is designed. The complex island partition model is solved to achieve accurate power supply reliability evaluation. And the incremental power supply capability associated to DG and ES devices is calculated. Finally, a case study is conducted on the PG&E 69-bus system to verify the effectiveness of the proposed method. It is found that with a 20% configuration ratio of ES devices, the power supply capability improvement brought about by 6 MW DG can reach about 773 kW.

Detection of Electric Vehicles and Photovoltaic Systems in Smart Meter Data

In the course of the switch to renewable energy sources, there is a shift from a few large energy sources (power plants) to a large number of small, distributed energy sources (e.g., photovoltaic systems) and energy storage devices (e.g., electric vehicles). This results in the need to know and identify these energy sources and sinks as soon as new devices are installed, in order to ensure grid stability. This paper presents an approach to identify energy sources and energy storage in smart meter data, using photovoltaic systems and electric vehicles as examples. For this purpose, the Pecan Street dataset is used, which has been extended by charging processes from the ACN dataset. The presented approach comprises a combination of a Convolutional Neural Network and a Multilayer Perceptron, which decides separately, on the basis of the smart meter data of a household, whether an electric vehicle and a photovoltaic system are present. It is shown that the combination of both classifiers achieves accuracy of 90.50% in the case of electric vehicle detection and 96.37% in the case of photovoltaic systems. It is also shown that the power levels lower than 0 kW in the case of the photovoltaic system and higher than 5 kW in the case of the electric vehicles have the largest influence on the output of the Multilayer Perceptron branch, which uses the power balance distribution as input.

Resilience Oriented Planning of Urban Multi-Energy Systems With Generalized Energy Storage Sources

In the last decade, a number of severe urban power outages have been caused by extreme natural disasters, e.g., hurricanes, snowstorms and earthquakes, which highlights the need for rethinking current planning principles of urban energy systems and expanding the classical reliability-oriented view. In addition to being reliable to low-impact and high-probability outages, power system should also have high level of resilience to withstand high-impact and low-probability (HILP) events. Compared with power system, multi-energy systems (MESs) have advantages in improving resilience through energy shifting across multiple energy sectors, a variety of generalized energy storage resources and thermal inertia of heat/cooling loads. This paper proposes a resilience-oriented planning method to determine optimal configuration of distribution level MES, e.g., urban energy supply systems, considering comprehensive impacts from supply, network and demand sides in MES. Impacts of energy shifting at supply side, pipe storage at network side and thermal inertia at demand side are described in the same linear modeling framework using energy hub (EH) model. Generalized energy storage resources including centralized and distributed energy storage devices, pipe network storage and building heat capacity are all modeled into centralized energy storage to facilitate an efficient configuration planning of MES.

Planning Method and Principles of the Cloud Energy Storage Applied in the Power Grid Based on Charging and Discharging Load Model for Distributed Energy Storage Devices

The cloud energy storage system (CES) is a shared distributed energy storage resource. The random disordered charging and discharging of large-scale distributed energy storage equipment has a great impact on the power grid. This paper solves two problems. On one hand, to present detailed plans for designing an orderly controlled CES system in a realistic power system. On the other hand, Monte Carlo simulation (MCS) is used for analyzing the load curves of five types of distributed energy storage systems to manage and operate the CES system. A method of its planning and the principles of CES for applied in a power grid, are presented by analyzing the impact based on five load curves including the electric vehicle (EV), the ice storage system, the demand response, the heat storage system, and the decentralized electrochemical energy storage system. The MCS simulates the random charging and discharging of the system over a five-year planned scaling of distributed energy storage from 2021 through 2025. The influence of distributed energy storage systems on power grid capacity, load characteristics, and safety margins is researched to summarize the applicable fields of CES in supporting large power grids. Finally, important conclusions are summarized and other research possibilities in this field are presented. This paper represents a significant reference for planners.

Distributed Energy Storage Devices for Grids

Distributed Energy Storage Devices for Grids

Optimal scheduling of vehicle-to-Grid power exchange using particle swarm optimization technique

Electric vehicles (EV) are the inevitable future of the transportation industry considering the current energy scenario and all the incentives and benefits they provide. Nonetheless, EVs exhibit a novel characteristic of a distributed energy storage device (ESD) owing to their large onboard batteries. This feature of EV can be utilized to provide many ancillary services by the virtue of vehicle-to-grid (V2G) operation. One such service is the minimization of load variation on the grid. Two key challenges, concerning the practical execution of the V2G operation, are the availability of EVs for V2G operation and their ever-changing State of Charge (SOC). Due to the mobility of EVs as a transportation medium, their availability is highly uncertain. This paper addresses these issues by introducing a scheduling scheme for V2G power exchange by considering the stochastic nature of EV grid connectivity. This is done by first developing an optimization algorithm using the particle swarm optimization technique, with input data that best represents the stochastic nature of EV availability. Then, the performance of the algorithm is evaluated by conducting several case studies. The results obtained for various case studies by performing simulations are represented and elaborated. Finally, the statistical analysis of the results signifies that the proposed V2G scheduling scheme can substantially flatten the load profile. Apart from the algorithm itself, another novel nature of the paper lies within the wide range of analyses carried out to study the effect of such scheduling schemes. These studies include the effect on the SOC level of different EVs, the effect on price fluctuation, time complexity analysis of the algorithm, etc.

Intelligent energy scheduling in a microgrid with custom power devices

The increasing penetration of distributed generators may harm power system and its control and operations. The microgrid offers a better solution for the issues in grid operation. Undergraduate electrical engineering and postgraduate power system and power electronics students need to understand the working of microgrid with renewable energy resources along with the optimization and scheduling of power quality custom power devices. To cater better understanding of the microgrid with renewable resources, wind, solar, distributed generators, distributed energy storage devices, DSTATCOM, and plug-in electric vehicle charging load are included in this work presented. In this paper, multiobjective optimization for the operation of microgrid to account for uncertainties in a stochastic energy resources management system is considered. Nonlinear constraint optimization to reduce the running cost, losses, and voltage variations is focused for the optimization. Loss sensitivity analysis is used to find the location wind, solar, distributed generators, distributed energy storage devices, and DSTATCOM. Firefly algorithm is used to optimize the size of distributed generators and DSTATCOM. Optimization reduced the loss, power taken from the grid and improves the voltages in the system. This paper is self-explanatory to the undergraduate and postgraduate students to understand the power flow analysis of microgrid with hybrid renewable energies and optimization technique.

A Mixed Integer Conic Model for Distribution Expansion Planning: Matheuristic Approach

This paper presents a mixed-integer conic programming model (MICP) and a hybrid solution approach based on classical and heuristic optimization techniques, namely matheuristic,to handle long-term distribution systems expansion planning (DSEP) problems. The model considers conventional planning actions as well as sizing and allocation of dispatchable/renewable distributed generation (DG) and energy storage devices (ESD).The existing uncertainties in the behavior of renewable sources and demands are characterized by grouping the historical data via the k-means. Since the resulting stochastic MICPis a convex-based formulation, finding the global solution of the problem using a commercial solver is guaranteed while the computational efficiency in simulating the planning problem of medium- or large-scale systems might not be satisfactory. To tackle this issue, the subproblems of the proposed mathematical model are solved iteratively via a specialized optimization technique based on variable neighborhood descent (VND) algorithm. To show the effectiveness of the proposed model and solution technique, the 24-node distribution system is profoundly analyzed, while the applicability of the model is tested on a 182-node distribution system.The results reveal the essential requirement of developing specialized solution techniques for large-scale systems where classical optimization techniques are no longer an alternative to solve such planning problems.

Distributed energy storage node controller and control strategy based on energy storage cloud platform architecture

Based on the energy storage cloud platform architecture, this study considers the extensive configuration of energy storage devices and the future large-scale application of electric vehicles at the customer side to build a new mode of smart power consumption with a flexible interaction, smooth the peak/valley difference of the load side power, and improve energy efficiency. A plug and play device for customer-side energy storage and an internet-based energy storage cloud platform are developed herein to build a new intelligent power consumption mode with a flexible interaction suitable for ordinary customers. Based on the load perception of the power grid, this study aims to investigate the operating state and service life of distributed energy storage devices. By selecting an integrated optimal control scheme, this study designs a kind of energy optimization and deployment strategy for stratified partition to reduce the operating cost of the energy storage device on the client side. The effectiveness of the system and the control strategy is verified through the Suzhou client-side distributed energy storage demonstration project.

Improved gray wolf optimization algorithm for solving placement and sizing of electrical energy storage system in micro-grids

Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Micro-grids are small-scale networks at low voltage levels that are use to provide thermal and electrical loads of small locations where there is no access to the main electrical grid. Given the environmental and economic issues for these areas, micro-grids can be a good solution for energy production. In this paper, determining the size and location of optimal electrical energy storage systems is presented. In other side, a new method based on the cost benefit analysis for optimal sizing of an energy storage system in a microgrid (MG) is proposed. The uncertainties associated with renewable energy sources and the occurrence of defects in the grid connection network and the effect of the contribution of load responses in a micro-grid are taken into account. The combined system consists of wind turbines and fuel cells. Basically, wind power is not definitively available. The new proposed method is based on two-stage randomization design (TSRD) for modeling the effect of wind power uncertainty so that the predicted wind energy error is considered as the main random parameter in the model. A standard probability distribution function is used to represent the error variations. Given the continuity of the mentioned function, the probability error function is extracted using the new discrete method and a certain number of scenarios with a certain probability. Finally, the problem has been transformed into an optimization problem, and a gray wolf optimization (GWO) algorithm has been used to solve it. In the proposed developed model based on local and global search, the algorithm tries to reach the final result in the shortest possible time and with the most precision. The results of the simulation show the efficiency of the proposed method in solving the micro-grid problem.

A Simple High-Performance Current Control Strategy for V2G Three-Phase Four-Leg Inverter With LCL Filter

Electric vehicles (EVs) can behave as distributed energy storage devices for providing on-demand smart grid support service, that is, an emerging vehicle-to-grid (V2G) technology. A high-performance and easy-implementation current control strategy for V2G three-phase four-leg inverter with LCL filter is proposed. It consists of a deadbeat (DB) controller and a paralleled repetitive controller (RC). The DB controller is based on the weighted average inductor current (WAIC) scheme, which simplifies the third-order LCL filter to be an equivalent first-order $L$ filter. The stability of the DB-controlled inverter with the unmodeled system time delay is analyzed. The DB controller is of very fast response and easy implementation but is not immune to system time delay and various uncertainties. To overcome the disadvantages, a plug-in RC is added to reinforce the DB controller to remove harmonic distortion from the feed-in current in the presence of parameter uncertainties. A lab prototype of 10-kW grid-connected three-phase four-leg inverter has been built up to validate the proposed current control strategy. The simulations and experiments are provided to demonstrate the validity of the proposed control strategy.

Guest Editorial Special Issue on New Trends in Energy Internet: Artificial Intelligence-Based Control, Network Security, and Management

Energy Internet is recognized as a new and advanced paradigm of smart grids, where energy collection devices, distributed energy storage devices, and various types of energy nodes are interconnected by applying advanced power electronics technology, information technology, and intelligent management technology. This emerging Energy Internet brings remarkable improvement for the society from various aspects with its advantages in high efficiency, strong flexibility, great scalability, and improved reliability. However, it in turn generates new challenges in architecture design, control operation, and energy management. As a result, how to deal with these challenges in Energy Internet still needs further investigation by applying advanced techniques, such as multiagent systems, artificial intelligence-based control, big data cloud computing and management, and so on.

Energy Optimization Dispatching of Microgrid in The Stand-alone Mode

A microgrid system in the stand-alone mode with photovoltaic, wind turbine, microturbine, fuel cell and energy storage unit is studied. Mathematical models of different distributed power supply and energy storage devices are established. The load demand of the system, the price of purchasing and selling electricity when interacting with the power grid and other constraints are considered. An economic model for optimizing stand-alone operation cost is established. According to the characteristics of the model, an improved genetic algorithm is selected to optimize the nonlinear system. The algorithm has better stability in solving the optimization problem of large state space dimension. The case study show that the proposed economic model is reasonable and the algorithm is effective.

Time-of-use pricing model based on power supply chain for user-side microgrid

The user-side microgrid offers great potential for improving energy efficiency. This flexible and small-scale power system is characterized by multiple types of clean power supplies. The power supply chain can reveal the supply-demand relationships in the series of steps from power generation to consumption. However, electricity prices tend to vary and can significantly affect the actual energy costs to end-users and other entities in the power supply chain. In this study, we propose an optimization model of time-of-use pricing for the user-side microgrid from the perspective of power supply chain management. The objective of this model is to minimize the total cost of the power supply chain and optimize the charging-discharging behaviors of end-users. First, to reduce the impact of demand amplification and variation, we investigated the bullwhip effect of the power supply chain. Then, we considered distributed energy storage as an important component of the user-side microgrid and how electric power companies can utilize pricing strategies to optimize the charging-discharging behaviors of end-users. We performed experiments based on two scenarios that assumed end-users with and without distributed energy storage devices. A comparative analysis of the modelling results indicates that optimal time-of-use pricing can support the charging-discharging behaviors of residential users and reduce the cost of the entire electric power supply chain. The optimized time-of-use price is important for stability, flexibility, and efficiency improvement in both the user-side microgrid and the entire power supply chain.

Time-of-use price model for user-side micro-grid based on power supply chain management

From the perspective of power supply chain management, an optimized model for user-side micro-grid time-of-use (TOU) price is established. The TOU price is designed by electric power enterprise for users with distributed energy storage devices to optimize their discharging behaviour. The objective of the TOU price is to minimize the total cost of power supply chain under the constraints of power system operation and the constraints of user side distributed energy storage devices. The results of the case study indicate that the optimal TOU price obtained from the proposed model can not only instruct users to discharge the electricity storage orderly and guarantee the stability of distributed energy resources to grid, but also reduce the waste in total cost of power supply chain. Therefore, it can support the power company for load scheduling, and further improve the safety, efficiency, and flexibility of power grid operation.

Over Current Protection Relay using Arduino Uno for Future Renewable Electric Energy Delivery and Management (FREEDM) System

The FREEDM (Future Renewable Electric Energy Delivery and Management) system is a smart grid that enables wide integration between the Distributed Renewable Energy Resources (DRER) and Distributed Energy Storage Devices (DESD) with the conventional distribution system. This paper presents the design and implementation of an Arduino Uno microcontroller-based overcurrent relay with different characteristics (inverse, very inverse and extremely inverse) for FREEDM systems. An open source model with simple utilization of both hardware and software is created. A practical printed circuit board is designed with the required inputs and outputs to monitor and protect the branch connecting solid state transformer (SST) to the closed loop zones in the FREEDM system. A special program is designed using Proteus software package and easily integrated to the hardware card. To validate the proposed relay, the inverse, very inverse and extremely inverse overcurrent relay characteristics are tested using the proposed system simulator and compared with the characteristic recorded by the well-known IEC 60255-151standard. In order to guarantee the effectiveness of the system, a practical circuit including the proposed relay is formed, connected to a small load (motor) and normally inverse relay characteristic is tested. The proposed protection scheme proves high performance and accurate results.

Toward the Next Generation of Multiperiod Optimal Power Flow Solvers

Distributed energy storage devices are commonly employed as an effective approach for addressing operational challenges introduced by the large scale integration of renewables. However, the modeling of storage devices results in intertemporal coupling of the individual optimal power flow (OPF) problems defined at each subdivision of the time period of interest. The resulting multiperiod optimal power flow (MPOPF) problem becomes intractable prohibiting, forecasting, and planning over long time periods. Interior point (IP) methods have been extensively employed for the solution of OPF and MPOPF problems. This work proposes an efficient IP algorithm, BELTISTOS, particularly designed for MPOPF problems. The structure of the linear system associated with the Karush-Kuhn-Tucker conditions is revisited, and a Schur-complement-based approach tailored to its structure is proposed. Through benchmark cases involving power-grid models of increasing complexity, the BELTISTOS algorithm is demonstrated to provide several orders of magnitude faster solution times than standard optimization methods, such as IPOPT, MIPS, and KNITRO, using significantly less memory.