Energy management of a renewable-based isolated micro-grid by optimal utilization of dump loads and plug-in electric vehicles

With the new challenges brought by the high penetration of Renewable Energy Resources (RESs) into the modern grid, developing new solutions and concepts are necessary. Microgrid (MG) is one of the new concepts introduced to overcome upcoming issues in the modern electricity grids. MGs and Multi-Microgrids (MMGs) are defined as the building blocks of smart grids. MGs are the small units, where power generation and consumption happen at the same location and MG makes the decisions by itself. MGs can operate grid-connected or island mode depending on the functionality of the MG. Energy Management System (EMS) is the decision making centre of the MG. The data from the devices is received by the EMS and after processing, the commands are sent to the controllable components. Management of voltage, active and reactive power, neutral current, unit commitment and economic dispatch are of the tasks of EMS. In this PhD thesis, an optimal EMS for MGs and MMGs is developed. The main objective of this project by developing the EMS is to optimize the energy flow in the MGs and MMGs to obtain peak load shaving in a cost beneficial system. In order to achieve an efficient EMS, communication system, forecasting system, scheduling system, and optimization system are modelled and developed. Different types of EMS operation, centralized, decentralized and distributed, are investigated in this work to achieve the best combination for MMG EMS operation. The communication system is mainly utilizing Modbus TCP/IP protocol for data transmission at local level and Internet of Things (IoT) protocols (MQTT) for the global communication level. A communication operation algorithm is proposed to manage the MMG EMS under different communication operation modes and communication failure conditions. Furthermore, a monitoring system is developed to collect the data from different devices in the MG. The data is processed in the MG EMS and the commands are sent to components through the communication infrastructure. The link between MGs and MMGs is through the proposed two-level communication system, where the expansion of MGs to a MMG is investigated. In the MMG, MGs are functioning as a unit while having different priorities and operating under different policies. Each MG has its own MG EMS and the EMSs transfer information through the communication system between each other in either centralized, decentralized, distributed, or no communication modes under the MMG EMS. The forecasting system is required in the EMS to predict the future MG characteristics such as power generation and consumption. The forecasted data is the input to the optimization and scheduling system of EMS. Employing the forecasting system in the EMS would increase the accuracy of the optimization and scheduling systems. In this thesis, the timeseries-based forecasting algorithms are employed to predict next day’s active power using the load data, generation data, weather data and temperature data as the inputs. The heart of EMS is the scheduling and optimization system. The purpose of the scheduling system is to define the amount and the time of energy flow in the MG for different generation sources and consumption loads. Furthermore, scheduling system is responsible for peak load shaving and valley filling. On the other hand, the optimization system has the task of minimizing the operation costs of the MGs. The role of market in the scheduling and optimization is important. Time of Use (ToU) tariff is the pricing system, which determines the peak and off peak hours for energy usage pricing. In order to apply the optimization system, a model of the system, an objective function and systems constraints are defined, where aging of battery energy storage system (BESS), operational cost of components and MG cost benefits are considered. To operate the EMS scheduling and optimization system, IBM CPLEX Optimization Studio solver conducts the optimization while for the scheduling system, objective function and constraints are defined in MATLAB. In this thesis, a rule-based, MILP and MIQP optimization system for commercial MGs including electric vehicles (EVs) are proposed to investigate performance of MG EMS for different case studies. In this thesis, the literature for different scheduling and forecasting systems is investigated and different optimization algorithms are analysed. The communication protocols utilized in this research are described and compared to other protocols in the literature. In different chapters of this thesis, the modelling of MGs and MMG EMS, different modules of EMS, forecasting, optimization, scheduling and communication systems are described and analysed. A novel communication system for MMG EMS operation is proposed for commercial buildings. The performance of MG EMS and MMG EMS is examined for power and neutral current sharing, operation cost optimization, and demand peak shaving applications and results are compared to investigate the performance of proposed algorithms.

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.

A microgrid (MG) is an energy system composed of renewable resources, energy storage unit and loads that can operate in either islanded or grid-connected mode. Renewable resources should be scheduled to manage load demand and power flow within MG. This paper presents a MG energy management system (M-EMS) for grid-connected photovoltaic (PV) and battery energy storage system (BESS) based hybrid MG. The proposed M-EMS consists of two modules, namely, forecasting and optimisation. The forecasting module is responsible for predicting solar irradiance, temperature and load demand, whereas the optimisation module performs optimal day-ahead scheduling of power generation and load demand in a grid-connected MG for economical operation. The proposed M-EMS for grid-connected hybrid PV-BESS MG is verified using MATLAB/Simulink. Simulation results indicate the efficiency and effectiveness of the proposed method for understudy case.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

Balancing the active power between the generation side and the demand side to maintain the frequency is one of the main challenging problems of integrating the increased intermittent wind power to the smart grid. Although the energy storage system, such as battery energy storage system (BESS), has potential to solve this problem, the installation of the BESS with large capacity is limited by its high cost. This chapter investigates the frequency regulation of the smart grid working in the isolated mode with wind farms by introducing not only the BESS but also dynamic demand control (DDC) via controllable loads and the plug-in electric vehicles (PEVs) with vehicle-to-grid (V2G) service. First, modelling of a single-area load frequency control (LFC) system is obtained, which includes the wind farms equipped with variable-speed wind turbines, the simplified BESS, the air conditioner based DDC and the distributed PEVs. The LFC system contains traditional primary and supplementary control loops and three additional control loops of the BESS, the PEVs and the DDC, respectively. Then, state-space models of the closed-loop LFC scheme with/without communication delays in the control loops are derived, and the stability of the closed-loop system with time delays is investigated via the Lyapunov functional based method. Third, gains of proportional integral derivative (PID)-type controllers are tuned based on the H∞ performance analysis and the particle swarm optimization searching algorithm. Case studies are carried out for the single-area smart power grid through the MATLAB®/Simulink platform. Both the theoretical analysis and the simulation studies demonstrate the contribution of the DDC, the BESS, and the PEVs to frequency regulation, and the robustness of the designed PID-type LFC against the disturbances caused by the load changes and the intermittent wind power and the delays arising in the control loops via theoretical analysis and the simulation studies.

The optimization of energy systems within a multi-microgrid framework, enriched by shared Battery Energy Storage Systems (BESS), has emerged as a compelling avenue for enhancing the efficiency of distributed energy networks. In response to the increasing integration of BESS in modern energy systems, this study investigates the implications of incorporating BESS within connected residential-commercial Microgrids (MGs). Unlike previous studies that primarily focused on cost and reliability, this research fills a significant gap in the literature by investigating the optimization of load demands patterns. Specifically, we explore the impact of shared BESS on load demand patterns in commercial-residential MGs. The research introduces two innovative critical load metrics, peak-to-average ratio (PAR) and demand profile smoothness (DPS), to assess the influence of BESS on demand profiles. In addition, the study explores the integration of a Dynamic Thermal Rating (DTR) system, compared to traditional fixed thermal rating systems, to further optimize the performance and efficiency of connected residential-commercial MGs enriched by shared BESS. Three distinct case studies, each comprising a commercial MG (shopping mall, hotel, and office building) paired with a residential MG, were considered. Utilizing a Firefly Algorithm (FA) for optimization, the study determines the optimized BESS capacity for minimum total cost. The results highlight that the implementation of shared BESS, especially in collaboration between commercial and residential MGs, significantly reduces imported energy from the main grid, enhancing MG flexibility and resilience. While the economic benefits of shared BESS may not be substantial (up to 3.25 % cost reduction), the study underscores its contribution to more balanced and smoother load demand curves, with improvements in PAR (up to 15.63 %) and DPS (up to26.05 %). Moreover, considering DTR for transmission lines, instead of fixed thermal rating, improves the PAR and DPS up to 28.52 % and 41.06 % respectively. The findings of this study highlight the importance of considering load demand patterns in the design and operation of MGs and underscore the multifaceted benefits of shared BESS beyond economic considerations.

This paper proposes a fuzzy logic-based energy management system (EMS) for microgrids with a combined battery and hydrogen energy storage system (ESS), which ensures the power balance according to the load demand at the time that it takes into account the improvement of the microgrid performance from a technical and economic point of view. As is known, renewable energy-based microgrids are receiving increasing interest in the research community, since they play a key role in the challenge of designing the next energy transition model. The integration of ESSs allows the absorption of the energy surplus in the microgrid to ensure power supply if the renewable resource is insufficient and the microgrid is isolated. If the microgrid can be connected to the main power grid, the freedom degrees increase and this allows, among other things, diminishment of the ESS size. Planning the operation of renewable sources-based microgrids requires both an efficient dispatching management between the available and the demanded energy and a reliable forecasting tool. The developed EMS is based on a fuzzy logic controller (FLC), which presents different advantages regarding other controllers: It is not necessary to know the model of the plant, and the linguistic rules that make up its inference engine are easily interpretable. These rules can incorporate expert knowledge, which simplifies the microgrid management, generally complex. The developed EMS has been subjected to a stress test that has demonstrated its excellent behavior. For that, a residential-type profile in an actual microgrid has been used. The developed fuzzy logic-based EMS, in addition to responding to the required load demand, can meet both technical (to prolong the devices’ lifespan) and economic (seeking the highest profitability and efficiency) established criteria, which can be introduced by the expert depending on the microgrid characteristic and profile demand to accomplish.

Supply energy management methods for a <scp>direct current</scp> microgrid: A comprehensive review

An energy management system of a microgrid (MG) has several basic objectives; e.g. to maximize the utilization of renewable energy resources (RES), to protect the internal components from overloading, and to ensure that the MG operates reliably under any operating conditions. Although many control techniques are available in the literature to monitor and control the energy flows among distributed RES in MGs, formal verification of those techniques was not proposed yet. The emphasis of this paper is to design and validate energy management system for a MG which consists of a solar photovoltaic (PV) array, a pair of battery energy storage systems (BESes), a diesel generator (DG) and a load (LD). The physics and dynamics of the MG are defined as energy flow invariants and the designed behaviours are abstracted, modelled and validated in this work. Therefore, we have considered an invariant based flow technique to manage the energy flow in an MG. The results are validated and verified with UPPAAL, a powerful industrial tool which is commonly used to verify the correctness of real-time systems like supervisory controllers, communication protocols and others.

Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach

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.

&lt;span&gt;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.&lt;/span&gt;

Advanced active disturbance rejection control for enhancing frequency stability in low-inertia power grids linked with virtual inertia applications.

In this paper, optimal economic management of a grid-connected microgrid (MG) with distributed energy resource (DER) and its interaction with incentive-based demand response programs (DRPs) is studied. The use of DR makes energy management system (EMS) of the MG an efficient tool in balancing the demand and supply, and therefore ensuring the network reliability. In this work, the cost function of customers is developed in the incentive-based DRP with the aim of receiving a more realistic incentive and then it is combined with EMS. Accordingly, the consumers offer hourly power reduction bids based on which they are sorted and then incentive-based payment model is implemented. At times, due to full utilization of grid and MG resources, the supply–demand balance cannot be maintained by respecting the consumer offers. Specific energy policies and contracts are required in this case for mandatory power curtailment in exchange for higher incentive payments by MG operator (MGO). The objective function attempts to minimize operation costs of the MG units such as Diesel Generator fuels costs, cost of power exchange with the main grid, battery energy storage system (BESS) costs and in the mean time, maximize MGO DR benefit. On the other hand, simultaneous EMS and DR management leads to a complex non-linear problem, which can be solved using whale optimization algorithm (WOA) in MATLAB software. To assess the performance of the proposed new approach, a grid-connected MG with DERs and reducible power of consumers is studied within a 24-h time cycle. Also, to verify the scalability of the implemented system, an MG with aggregators and a large scale battery is considered. Simulation results show that incorporating a developed DR into EMS is an efficient way in optimal performance of both demand and supply sides in conjunction with the goals of economic operation of MGs.

Microgrids (MGs) are the essential part of the modern power grids defined as the building blocks of smart grids. Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs) combined with Distributed Generators (DGs) form a comprehensive MG, which require the control and Energy Management System (EMS) to fulfill the load and grid requirements. As the need for BESS grows due to uncertainties of RESs, scheduling and cost management of BESSs in the MG becomes more of a concern. In this paper, BESSs have been designed for a university research center to simultaneously overcome the outage problem and shave the peak demand considering the BESS sizing and degradation; MG cost minimization, as well as MG scheduling. PV and wind are the RESs employed in this study and in combination; Li-Ion BESS has been utilized to investigate the MG performance. A two-layer optimization algorithm has been presented to optimally define the BESS size and minimize the operational cost of the MG achieving the peak shaving and valley filling objectives. The results prove the functionality and applicability of the proposed system to be implemented as a part of the experimental MG at Griffith University in order to enhance the stability and reliability of the research center and at the same time minimize the operational costs of the MG.