Enhancing smart grid transient performance using storage device‐based MPC controller

Guest Editorial: Situational awareness of integrated energy systems

Power systems have been undergoing radical changes in recent years, and their planning and operation will be surely undertaken according to the Smart Grid (SG) vision in the near future. The SG initiatives aim at introducing new technologies and services in power systems, to make the electrical networks more reliable, efficient, secure and environmentally-friendly. In particular, it is expected that communication technologies, computational intelligence and distributed energy sources will be widely used for the whole power system in an integrated fashion. In particular, nowadays, unprecedented challenges like as stringent regulations, environmental concerns, growing demand for high quality, reliable electricity and rising customer expectations are forcing utilities to rethink about electricity generation and delivery from the bottom up. Moreover, the availability of low cost computing and telecommunications technologies, new generation options, and scalable, modular automation systems push utilities to be dynamic, innovative and ambitious enough to take advantage of them. Driven by the dynamics of the new energy environment, leading utilities, technology vendors and government organizations have created a vision of the next generation of energy delivery systems: the Smart Grid. Operational changes of the grid, caused by restructuring of the electric utility industry and electricity storage technology advancements, have created an opportunity for storage systems to provide unique services to the evolving grid. Especially Battery Energy Storage Systems (BESSs), thanks to the large number and variety of services they can provide, are powerful tools for the solution of some challenges that future grids will face. This consideration makes BESSs critical components of the future grids. The BESS can be applied for different services into the different levels of power system chain to satisfy technical challenges and provide financial benefits. In the context of the application of BESSs in SGs, there are two main problems that need to be addressed in a way that exploits the BESS potential, that are linked to their operation and sizing. This thesis focuses on both these aspects, proposing new strategies that allow optimizing the BESS adoption. When dealing with BESSs, sizing and operation are strictly linked. The correct sizing of a BESS, in fact, needs to take into account its operation which in turn will be effected with the aim of optimizing the whole system where it is included. In the first part of this research study, advanced optimal operating strategies were proposed for BESSs by considering both the distribution system operator perspective and the end user. Thus, the proposed operating strategies were performed with the aim of (i) leveling the active power requested by the loads connected to a distribution system (distribution system operator service), (ii) reducing the electricity costs sustained by an end-use costumer that provides demand response (DR) (end user service) and (iii) scheduling a microgrid (µG) with DR resources such as Plug-in Electric Vehicles (PEVs) and Data Centers (DCs) (both the two section service). The proposed strategies also satisfied technical constraints of BESSs and other components of the µG. The second part of the thesis presented the optimal sizing of BESSs aimed at maximizing the benefits related to their use. In the thesis, the sizing, which is performed by considering the end user point of view with reference to both the industrial and residential customers, is effected by adopting both deterministic and probabilistic approaches. With reference to the deterministic approach, a simple and quick closed form procedure for the sizing of BESSs in residential and industrial applications was proposed. In case of probabilistic approach, the case of a BESS installed in an industrial facility was considered and the sizing was performed based on the decision theory. Technical improvements and economic benefits of optimal operation and optimal sizing of BESSs in SG are demonstrated by the obtained results which are reported in the numerical applications. More specifically, it was clearly determined that BESSs can offer technical supports into the distribution operator section of the grid in terms of load management and security challenges. Moreover optimal integration of BESSs into the grid was also appealing for end users thanks to valuable amounts of electricity bill cost reduction. Regarding the original contribution of the thesis, the following considerations can be done. With reference to the load leveling service, an innovative two-step procedure (day-ahead scheduling and very short time predictive control) was proposed which optimally controls a BESS connected to a distribution substation in order to perform load leveling. In case of DR, a proper control of the BESS was proposed in order to perform DR under different price schemes, such as Real Time Pricing (RTP) and Time of Use (TOU) without modifying the daily work cycle of the industrial loads. The control procedure allows achieving contemporaneously two important goals that are the reduction of the bill costs and the prolonging the battery's lifetime so further reducing the costs sustained by the customer. With reference to the scheduling of microgrids, the original contribution of the thesis is focused on the proposal of optimization strategies aimed at managing and coordinating, simultaneously, batteries on board of vehicles or equipping data centers' Uninterruptable Power Supply (UPS) and Distributed Generation (DG) units. Also comparisons among different single-objective based strategies are made in order to highlight the most convenient. With reference to the sizing based on deterministic approach, unlike the other relating literature, the innovative contribution is that the closed form procedure takes into account both the technical constraints of the battery and contractual agreements between the customer and the utility. Moreover, in the economical analysis performed for the sizing, which is applied with reference to both residential and small industrial customers and is based on actual TOU tariffs, a wide sensitivity analysis to consider different perspectives in terms of life span and future costs was performed. Some aspects that affect the profitability of the battery, such as technological limitations (e.g. the battery and converter efficiency), economic barriers (e.g. capital cost and the rate of change of the cost) and variation of the load profile along the years were deeply analyzed. In case of sizing based on probabilistic approach, the original contributions of the thesis are mainly referred to the proposal of a new method that uses a decision theory-based process to obtain the best sizing alternative considering the various uncertainties affecting the sizing procedure. The thesis is organized in three chapters which are dealing with integration of BESSs in SGs. The first chapter reports basic concepts and characteristics of BESSs, fundamental components and features of SGs and different services that BESSs can provide. The optimal operation strategies of BESS are considered in second chapter which includes their problem formulation, solving procedures and results. The third chapter deals with the optimal sizing problem of BESSs for which the problem formulation, solving procedures and results are reported. Finally, the conclusions are presented in the last part of thesis.

Accelerated development of eco-friendly technologies, such as renewable energy (RE), smart grids, and electric transportation will shape the future of electric power generation and supply. The power consumption characteristics of modern power systems are designed to be more flexible and easily controllable, which will also affect the sizing of power generation system. This paper presents a methodology for the joint capacity optimization of a typical residential standalone microgrid (MG) employing RE sources, i.e., solar photovoltaic (PV), wind turbines (WTs), diesel generators (DGs), and battery energy storage system (BESS). The MG supplies a residential community load comprising of typical residential load plus electric vehicles (EVs) charging load. The realistic mathematical models of PV, WT, diesel generation system, BESS, and EV load are formulated to improve the capacity optimization methodology, which involves various realistic constraints associated with the RE sources, diesel generation system, BESS, and EV load. The labyrinthine optimization problem is formulated and solved innovatively to 1) minimize the cost; 2) reduce greenhouse gases (GHG) emissions; and 3) curtail dump energy. All three objectives have special significance in designing a standalone MG, for example, cost is related to the economics, GHG emissions deal with global warming, and dump energy is related to the stability and economics of the system. The optimization problem is solved for different possible combinations of PV, WT, DG, and BESS to determine the best possible combination to serve the load effectively and economically. In addition, the impact of load shifting on the sizes of distributed generators and BESS in terms of per-unit cost and GHG emissions is analyzed using the concept of controllable loads. This study could be assumed as a powerful roadmap for decision makers, analysts, and policy makers.

Superconducting Magnetic Energy Storage (SMES) systems are one of the superconducting technologies with envisaged applications in power systems. The flexibility of these systems allows a utilization as Energy Storage Systems (ESS) but also as power devices, for power quality applications. In the actual context of electric grids, with the emergence of the paradigm of Smart Grids, the possible applications of SMES systems are discussed, considering the actual state of the art in superconducting materials. Main envisaged applications are presented and a short comparison with other ESS is also performed, as a way to integrate SMES systems into the actual and upcoming context of power systems. Main already running or finished projects using not only standalone SMES systems but also a combination of multiple superconducting devices are also presented. Finally, a short description of concept projects using SMES systems is also given.

A modern dc microgrid often comprises renewable energy sources (RESs), such as photovoltaic (PV) generation units, battery energy storage systems (BESSs), and local load, and it is also connected to the utility grid through a point of common coupling (PCC). While most existing approaches have to rely on communication links to achieve the desired control performance, this article proposes a novel control strategy without resorting to the communication links. This is achieved by assigning BESSs as master units and regulating the dc bus voltage with a novel state-of-charge (SoC)-based droop control, where the BESSs coordinate the slave units (e.g., RES and utility grid) with the aid of the dc bus signaling (DBS) technique to avoid overcharging and overdischarging of these BESSs. In the proposed droop control, the reference voltage for these BESSs is designed for coordinated operation between BESSs and utility grid, and it is maintained constant in the normal SoC range, which can reduce dc voltage variation. Droop coefficients designed for SoC balance of BESSs are dynamically adjusted based on their own SoC values. Furthermore, the preset maximum deviation between the reference voltage and the dc bus voltage ensures the reliable coordinated operation. Real-time hardware-in-loop (HIL) tests considering three different scenarios are conducted to validate the effectiveness of the proposed method.

In this paper, a model predictive control (MPC)-based coordinated scheduling framework for variable wind generation and battery energy storage systems (BESSs) is presented. On the basis of the short-term forecast of available wind generation and price information, a joint look-ahead optimization is performed by the wind farm and storage system to determine their net power injection to the electric power grid. In conjunction with moderate battery capacity, the excess unpredictable wind generation can be used to charge the battery storage and vice versa. The benefits of the proposed scheduling approach are that (1) the combined profit of wind generation and BESS is increased; (2) the net power injection from the wind farm into the power grid is smoothed out; and (3) the look-ahead optimization updates the price prediction in a moving horizon, which leads to more robust profit for wind farm and BESS against price uncertainties. By formulating the MPC-based coordinated scheduling as a quadratic programming problem, several numerically efficient algorithms to compute the optimal control strategy for wind generation and BESS are proposed. The effectiveness of the proposed algorithm in a modified IEEE 24-bus reliability test system with aggregated plug-in hybrid electric vehicles is demonstrated. It is shown that the proposed algorithm can increase the joint profit of wind farm and BESS while smoothing out the net power injection to the electricity grid. The proposed MPC-based scheduling problem can be solved in approximately 400 ms, which makes the framework implementable in realtime electricity market operations.

Uncertainty parameters of battery energy storage integrated grid and their modeling approaches: A review and future research directions

The penetration of renewable energy sources (RESs) in the electrical power system has increased significantly over the past years due to increasing global concern about climate change. However, integrating RESs into the power market is highly problematic. The output of RESs such as wind turbines (WTs) and photovoltaics (PVs) is highly uncertain. Their correlation with load demand is not always guaranteed, which compromises system reliability. Distributed energy resources (DERs), especially demand response (DR) programs and energy storage systems (ESSs), are possible options to overcome these operational challenges under the virtual power plant (VPP) setting. This study investigates the impact of using a DR program and battery energy storage system (BESS) on the VPP’s internal electricity market, and also cost-minimization analysis from a utility viewpoint. Three different constrained optimal power flow (OPF) problems are solved such as base case, DR case, and BESS case to determine total incurred costs, locational marginal prices (LMPs), and generator commitments. A scenario tree approach is used to model the uncertainties associated with WTs, PVs, and load demand. The proposed model is investigated on a 14-bus distribution system. The simulation results obtained demonstrate a favorable impact of DR and a BESS on renewable operational challenges.

This paper introduces battery energy storage systems (BESS) as one of the alternatives in dispatching power systems including renewable energy sources (RES) as well as thermal units. A stochastic based power dispatch model is proposed to optimize the operation of thermal units, RES and BESS involves the degradation cost of BESS. The RES operating cost considers the uncertainties of solar irradiance and wind speed by considering overestimation and underestimation penalty terms. The degradation cost of the BESS includes the starting and ending levels of the batteries’ state of charge as well as the discharge rate. Hybrid mean variance mapping optimization (MVMO-SH) algorithm is utilized to solve the dispatch problem. The model is tested using different case studies and the impact of RES as well as BESS penetration in power systems are investigated. The results show that there is a significant drop in the total operating cost caused by RES and BESS integration in the cases under study. This is attained by lessening the power drawn from thermal units.

The basic operation principle of a multiterminal superconductive magnetic energy storage (MSMES) system is introduced. The motivation for developing the MSMES systems is to combine and maximize the flexibility benefits provided by energy storage and the controllability benefits provided by power electronic systems. A MSMES system can be used simultaneously as an energy storage device and a power flow control device. This attribute enables MSMES systems to perform some unique functions in electric power systems. Potential applications of MSMES systems and their impact on solving the problems faced by power systems today are discussed. >

Renewable energy power has obvious volatility, uncertainty, and anti-peak shaving characteristics. For the power system which has already built pumped storage power stations, in order to improve the absorption capacity of large-scale renewable energy, a battery energy storage system (BESS) optimal capacity determination and a pumped storage - BESS joint economic operation strategy are proposed in this paper, realizing the function of renewable energy source (RES) output smooth, RES planned output curves tracking, system peak-valley power difference reduction. Firstly, considering the RES planned output power range and short-term power change limitation, the BESS power reference is extracted based on moving average filtering. Secondly, minimum requirement of BESS power and energy can be determined. Then, aiming at power system overall economic benefit maximized, the Hybrid Energy Storage System (HESS) optimal operation model and joint economic operation strategy are proposed. Finally, the feasibility and effectiveness of the proposed model and strategy is verified by a case.

Optimal placement of BESS in a power system network for frequency support during contingency

The usage of renewable energy to supply electricity demands is expected to increase drastically in the coming years. This is attribute to the encouragement of the government through policies such as RPS(renewable energy portfolio standards) and FIT(feed-in-tariffs). However, the performance of power system is sometimes critically constrained by stability characteristics. It is very challenging for system operators to manage a high penetration of renewable energy resources in a bulk power system, as they are known to deteriorate the dynamic performance of power systems.Therefore, it has been proposed that the large-scale Energy Storage System (ESS) of which power output can be controlled accurately and quickly can be applied to a bulk power system for securing the stability of the system despite a high penetration of the renewable energy resources. By increasing deployment of renewable energy, importance of power grid stability has been increased. For example, Wind Power has characteristics of difficult prediction and rapid variation depending on the regional climate characteristics. Energy Storage System aims to transform an uncontrollably variable and partially unpredictable renewable energy into a controlled and predictable one. For these purpose, a quickly responsive and highly efficient control-algorithm is essential. It is for using such a high performance of ESS when a disturbance occurs on a electrical grid system.KEPCO implemented a demonstration project on large-scale grid connected 4 MW / 8 MWh Battery ESS (BESS) using Lithium-Ion Battery (LIB). Fig. 1 shows the interconnection of ESS at the demonstration site. The 4 MW / 8 MWh BESS is connected to both the 22.9kV substation bus and the distribution line in Jocheon, Jeju Island for offshore wind turbines. In this study, Operation strategies for the BESS were developed for frequency regulation of the grid-system. Fig. 1 Interconnection of ESS at Jocheon Substation For power system frequency regulation, There are two kinds of control strategies through ESS here. One is for the normal status that the ratio of frequency change is not bigger than reference ratio of frequency change(ξ). The reference ratio is frequency changes per second when the smallest power supply was failed from the power system. The other is for the dynamic status that the ratio of frequency change is bigger than the reference ratio. In case of South Korea, it is about -0.306 Hz/sec. It could be variable every year because the total amount of power supply in the grid-system would be different. In this paper, we researched briefly about the normal status.At normal control mode, the power output(P) of the ESS is proportional to the change in frequency(f). kd is a proportional coefficient set by the droop characteristics of a battery generation.P=kd(60-f) In order to distribute output power of each battery, this study established a SOC(State Of Charge) weighted control strategy that can consider the SOC of each battery. The following factors were considered to provide frequency regulation service while considering the SOC :1) Set proportional coefficient (kd) for calculating total output demand2) Set priority at output distribution according to SOC3) Consider rating output of each battery when distributing outputAt normal status, the proportional coefficient(kd) was set by applying the droop of a general generator. This is to control the ESS output with a performance similar to the droop of the general generator. When allocating output demand determined by each battery SOC through this strategy, the battery with a higher SOC have priority in distribution. This is more efficient way to fulfill the total output demand cost-effectively. The SOC weighted control result is shown in Figure 2.Fig. 2 Result of battery output during SOC weighted control (simulation)It can be found that the distributed quantity of output was provided depending on the SOC level of each battery. It's likely to be seen that a battery with a higher SOC has priority in the distribution of output.SOC management effect of the control algorithm was analyzed through a 5-hour too. The result obtained from a simulation frequency of five hours before and after a credible accident. We can find that, in the range within dead band, the control of battery was conducted in order to reach a SOC sustainment section. When the frequency is in the dead band, ESS is not working for frequency regulation. In the outside range of the frequency dead band, it was observed that control capacity was distributed according to SOC in order to provide frequency regulation.

Efficient operation of battery energy storage systems, electric-vehicle charging stations and renewable energy sources linked to distribution systems

Massive integration of generations from renewable energy sources such as photovoltaic (PV) and wind power generations into power systems causes large frequency fluctuations because their outputs are intermittent in nature. Battery energy storage system (BESS) is considered as one of solutions to this problem. In operation of the BESSs, their characteristics should be taken into consideration. For example, to extend the life time of the BESS, its state of charge (SOC) should not reach the upper or lower limit. In addition, efficiency of the BESS inverter is degraded when its output is low. In this paper, when multiple sets of BESSs and different kinds of BESSs are installed into a power grid, a new control method of the BESSs for load frequency control taking into account their characteristics is proposed. Numerical simulations for the multi-machine power system model are carried out to show effectiveness of the proposed control method. It is made clear that their total inverter loss can be reduced and all of their SOC can be always kept within the limits while contributing to the frequency control by the proposed method.