Optimizing microgrid efficiency: Coordinating commercial and residential demand patterns with shared battery energy storage

<span>This paper proposes a new optimal operation of Microgrids (MGs) in a distribution system with wind energy generators (WEGs), solar photovoltaic (PV) energy systems, battery energy storage (BES) systems, electric vehicles (EVs) and demand response (DR). To reduce the fluctuations of wind, solar PV powers and load demands, the BES systems and DR are utilized in the proposed hybrid system. The detailed modeling of WEGs, solar PV units, load demands, BES systems and EVs has been presented in this paper. The objective considered here is the minimization of total operating cost of microgrid, and it is formulated by considering the cost of power exchange between the main power grid and microgrid, cost of wind and solar PV energy systems, cost of BES systems, EVs and the cost due to the DR in the system. Simulations are performed on a test microgrid, and they are implemented using GAMS software. Various case studies are performed with and without considering the proposed hybrid system.</span>

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.

This paper reviews the development of demand response (DR) and dynamic thermal rating (DTR) system for enhancing the operation and reliability of power system. The advantages and prospect of the DR program are discussed. The case for DTR system is established by comparing it against the traditional static thermal rating (STR) system. Various line monitoring methods and devices required for the implementation of the DTR system are presented. The challenges for deploying the DTR system from the perspective of selecting appropriate transmission lines for DTR deployment, identifying critical spans for deploying DTR sensors, managing the reliability of the DTR system, and the integration of the DTR system with existing and future power systems are discussed. Finally, the two main standards governing the operation of the DTR system, namely the IEEE 738 standard and the CIGRE standard are compared to elucidate the employability of the DTR system.

This paper proposes a joint optimization model of grid-tied residential microgrid (MG) planning and operation with the support of demand side management (DSM). In this model, the residential MG is included with various distributed energy resources, such as photovoltaic units, wind turbines, microturbines, diesel engine generators (DEs), and battery energy storage systems (BESSs). The proposed model is formulated as a bi-objective optimization problem in view of economic and environmental contemplation. Therefore, the minimization of MG total annual cost and total annual emission are considered as the primary objectives. This paper mainly analyzes the benefits of DSM assistance in the process of residential MG joint planning and operation optimization. The proposed bi-objective optimization problem is converted into a single-objective optimization problem and the single-objective problem is solved using a mixed integer linear programming method. To investigate the impacts of DSM on the MG planning and operation problem, the proposed model is applied to a residential MG consisting of 1000 smart homes with different DSM participation levels. The simulation results show that the proposed model is an efficient tool for residential MG planning and operation optimization from the perspective of economic and environmental considerations.

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.

This paper addresses design consideration on Battery Energy Storage System (BESS) sizing of Hybrid Electric Marine Vessel. Different size of BESS is simulated to assess fuel consumption of the vessel, charge/discharge peak power of the BESS, and equivalent life cycle of the BESS used. To determine power contribution between generators and BESS, a Power Management System (PMS) is designed based on load demand and battery remaining energy. In this paper, a ferry's load profile is used as the case study. From simulation results, there is not much reduction of fuel consumption when the BESS size is over 200kWh. However, the benefits of having larger BESS size are smaller required ratio of peak power to rated energy of the BESS and smaller equivalent life cycle used. Furthermore, the minimum BESS size is then determined whether the required ratio of peak power to rated energy can be met by commercially available battery. Different chemistries of lithium-ion based BESS technology are considered in this paper.

In this paper, a Markov model for reliability analysis of transmission lines equipped with a dynamic thermal rating (DTR) system is proposed. Moreover, a fuzzy procedure is proposed to determine an annual equivalent fuzzy DTR number to represent hourly variations of the line DTR. To implement the fuzzy annual DTR in composite system reliability analysis, an interactive method resolution technique for solving fuzzy optimization problems is developed. This method is chosen to address the fuzzy constraints associated with the lines equipped with the DTR system. Numerical results are presented using the 24-bus IEEE RTS, and demonstrate the validity of the proposed approaches.

This paper presents the control algorithm for Battery Energy Storage System (BESS) connected in Micro-Grid (MG), operating in grid-connected and islanded-mode. The MG consists of configurable units such as BESS, PV, diesel generator and load. The BESS is connected with Voltage Source Converter (VSC) for active and reactive power sharing in grid-connected mode. Two control scheme algorithms are presented. Firstly, inner current control loop and secondly outer active/reactive power (PQ) control loop is presented. Also the charging and discharging control of BESS is designed to maintain the SOC. In islanded-mode, Power-frequency (P-f) control of BESS is proposed to maintain the frequency of MG and supply power to the load. P-f control provides reference power for PQ control. BESS provide primary frequency control to MG; however, the low power capacity of BESS demands for a backup. Diesel generator provides a backup frequency control in islanded-mode, supply active/ reactive power to loads and charge the BESS. The control is designed in dq reference frame. The simulation results show the effectiveness of control system.

This paper presents a novel power flow optimization strategy for a Grid Connected microgrid (MG) equipped with a Battery Energy Storage System (BESS), namely a Li-Ion battery pack. A BESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. In the proposed system the MG is composed by an aggregation of distributed power generators and loads and a BESS is adopted to manage the power over-production/over-demand in real time, in order to maximize the prosumer profit looking at the current energy prices and the BESS State of Charge (SOC). The Energy Management System (EMS) is based on a Fuzzy Logic Controller (FLC) with a suitable rule inference system designed by an Expert Operator (EO). The control strategy is tested with different power profiles and BESS capacities in order to verify its effectiveness and limits. Furthermore, the FLC has been optimized by a Genetic Algorithm to increase the total profit exploiting the BESS as energy buffer. The optimization results have been compared to the initial FLC designed by the EO, taking into account both the profit and the deterioration of the BESS measured through a suitable battery stress index.

A novel approach for sizing thermal and electrical energy storage systems for energy management of islanded residential microgrid

Currently the operators of electrical distribution networks face a number of challenges, such as load growth, the proliferation of distributed generation and ageing infrastructure. This is drawing attention to techniques which will allow more efficient asset utilisation and carefully selected reinforcement projects. This paper presents the findings of research into the technical challenges and potential benefits of dynamic thermal rating (DTR) systems. The adoption of DTR systems has the potential to improve network capacity and in some cases to offer an effective alternative to traditional reinforcement methods. The research presented in this paper suggests that, when compared to traditional solutions of network reinforcement, a DTR system can, in some cases, offer a less invasive and more cost effective solution which allows the DNO to realise improved network component utilisation. (4 pages)

This paper presents the energy management in a solar PV based charging station integrated with battery energy storage system (BESS). The provision of optional exchange of power by the DC grid with the utility grid has been created here. Power flow through the utility grid is controlled using a voltage source converter (VSC). The loads considered are the Electric Vehicles (EVs) batteries. Load demand was met by the maximum power obtained from the solar PV (Photo Voltaic) using Maximum Power Point Tracking (MPPT) controller. An automated control of energy flow among solar PV, BESS and load battery is proposed using DC-DC converters. The dip in DC microgrid voltage, because of excess load demand beyond the available combined maximum power from solar PV and BESS, is compensated by delivering deficient power from utility grid. PSCAD simulation results are presented here to validate the proposed scheme.

<p><span>This paper proposes a methodology for designing and operating a microgrid (MG) for the main campus of the Technical Institution Hawija. In this MG, a battery energy storage system (BESS), photovoltaic (PV) generation system, and controllable loads are included. Due to the high penetration of the PVs, over-voltage issues may occur in this MG. A novel operation strategy is considered by coordinating the BESS, PVs, and loads to prevent power outages and accomplish a secure operation of this MG. In this proposed approach, droop controllers have been implemented to provide the appropriate references for the PVs and BESS to maintain the voltage of the MG within a secure range. The generation of the PVs may be curtailed to guarantee the fidelity of the voltage. The intended simulations will be based on MATLAB/Simulink to show the efficacy of the intended design.</span></p><script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML&delayStartupUntil=configured"></script><script id="texAllTheThingsPageScript" type="text/javascript" src="chrome-extension://cbimabofgmfdkicghcadidpemeenbffn/js/pageScript.js"></script>

In this article, a weighted-consensus strategy for state-of-charge (SoC) balancing, voltage regulation and accurate current sharing in a dc microgrid (MG) composed of battery energy storage systems (BESSs) is presented. The consensus strategy is applied in association with a S-shape function known as sigmoid function, a common activation function in neural networks. By a neighbor-to-neighbor communication, each BESS exchange its SoC information to achieve faster SoC balancing and uses only its capacity for accurate current sharing. The sigmoid functions ensure continuity and differentiability of the system around the charging/discharging of the BESSs and guarantee the SoC balancing even if the graph becomes disconnected by communication failures or if the consensus is switched-off. As each BESS uses its own information in the weighted-consensus, the resilience against attacks and communication time-delays is increased. When all SoCs are within operational limits, the strategy avoids circulating currents in the MG by forcing all BESSs either charge or discharge simultaneously. However, if the SoC limits are violated, simultaneous charge and discharge among BESSs in a low rate is allowed to satisfy the limits. Experimental tests show the feasibility of the proposed approach and its faster SoC balancing in relation to advanced methods in the literature.

As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.