Co-Simulation Platform for Intelligent Operation of Microgrid: A Multi-Agent Framework-Based Solution

Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

Various factors controlling health care in any country, but still there is a weakness in the infrastructure of health care systems in some developing countries, including many Arab countries, due to the shortage of medical specialists personnel, global economic crisis, and wars and disasters that struck many countries. A crying need for a tele-medical care becomes a necessity, and in order to overcoming this weakness we have proposed a multi-agent intelligent Telemedicine system in this research. The proposed system can help solve the shortage of specialists urgently and at lower costs. Furthermore, it can save the efforts of the patients' expensive travel for treatment abroad. Moreover, it can avoid infections from social closeness in times of disasters and pandemics.
 Nowadays Telemedicine systems have gradually become a popular medical model which have always attracted much attention due to the continuous development of the multi-agents' structure and capabilities of the artificial intelligence systems. So we will focus in the prototype design of our system on the algorithms, techniques and devices that support the multi-agents' structure and AI technologies. The Multi-agent approach happens to be a suitable structure for the development of Telemedicine systems. Worth mentioning it can solve problems that are difficult or impossible for an individual agent or a monolithic system, and the AI capabilities will minimize human errors and improve health care outcomes.
 We will start by a state-of-the-art literature survey as conducted on the E-health system architecture, AI capabilities, and multi-agent platform, and then we will illustrate prototype design of the suggested Multi-agent intelligent system for Telemedicine which will contribute in the development of the health care systems in the developing countries.
 Keywords: Healthcare system; Medical System; Artificial Intelligence; Multi-Agent, Telemedicine; Telehealth.

Regarding the advances in technology and anxieties around high and growing prices of fossil fuels, government incentives increase to produce cleaner and sustainable energy through distributed generations. This makes trends in the using microgrids which consist of electric demands and different distributed generations and energy storage systems. The optimum operation of microgrids with considering demand-side management increases efficiency and reliability and maximize the advantages of using distributed generations. In this paper, the optimal operation scheduling and unit commitment of generation units installed in a microgrid are investigated. The microgrid consists of technologies based on natural gas that are microturbine and phosphoric acid fuel cell and technologies based on renewable energy, including wind turbine and photovoltaic unit along with battery energy storage system and plug-in electric vehicle commercial parking lot. The goal of the paper is to solve a multi-objective problem of maximizing revenues of microgrid operator and minimizing emissions. This paper uses an augmented epsilon constraint method for solving the multi-objective problem in a stochastic framework and also implements a fuzzy-based decision-maker for choosing the suitable optimal solution amid Pareto front solutions. This new model implements the three type of the price-based and incentive-based demand response program. It also considers the generation reserve in order to enhance the flexibility of operations. The presented model is tested on a microgrid and the results demonstrate the efficacy of the proposed model economically and environmentally compared to other methods.

This study tackles a key challenge in modern energy management: how to optimize energy distribution when expanding the network is not economically or practically feasible. It explores the integration of hybrid renewable energy sources into a microgrid (MG) and proposes an energy dispatch strategy for MGs operating in both grid-connected and standalone modes. The study incorporates various energy sources, including solar panels (PV), wind turbines (WT), fuel cells (FC), microturbines (MT), diesel generators (DG), and energy storage systems (ESS). Unlike many existing studies that focus only on reducing operating costs, this research also considers energy losses, environmental impacts, and demand response—a vital but often overlooked factor. The research introduces a new method using a mixed-integer linear programming approach to solve the microgrid energy management (MGEM) problem. This method provides a multi-objective solution that includes demand response scheduling and optimizes factors such as PV and WT capacities, energy storage strategies, battery usage, power exchange with the grid, and overall costs and environmental impacts. When compared to leading optimization algorithms, the proposed approach showed better performance. The study also highlights the benefits of demand response programs in improving MG operations. For instance, using a Real-Time Pricing (RTP)-based demand response program reduced operating costs by 3.31%, emission penalties by 2.61%, and power losses by 0.62%. Similarly, a Direct Load Control (DLC)-based program achieved reductions of 2.25%, 2.1%, and 3.56%, respectively. This work advances MG energy management by addressing overlooked factors and demonstrating the benefits of integrating demand response programs into energy optimization strategies.

Microgrids (MGs) enhance modern power systems by improving reliability, efficiency, and renewable energy integration. However, their reliance on advanced control systems and communication networks makes them susceptible to cyberattacks, particularly false data injection attacks (FDIAs). This chapter addresses the threat of malicious load shedding, where attackers manipulate data to falsely indicate excessive power consumption in specific MG areas, triggering unnecessary load shedding and causing significant disruptions and economic losses. To combat this threat, a novel methodology employing blockchain technology is proposed to secure data integrity and authenticity within MG control systems. Leveraging blockchain’s decentralized and tamper-resistant nature makes sure of immediate detection of data alterations, preventing unauthorized manipulations. The effectiveness of this methodology is assessed by utilizing the modified IEEE-13 BTS. Comprehensive simulations demonstrate how blockchain enhances MG resilience against FDIAs, ensuring accurate data representation and reliable decision-making. This chapter details the technical mechanisms of malicious load shedding, the implementation of blockchain within MG infrastructure, and the impacts on consumers and critical services. The obtained simulation results from the modified IEEE-13 BTS highlight the robustness of this approach in detecting and preventing FDIA, thus mitigating unnecessary load shedding and maintaining trust in MG reliability.

A microgrid (MG) is always prone to the uncertainties of its demand variation and generation of its non-dispatchable renewable sources, particularly when operating in the islanded mode. Such events can push voltage and/or frequency of the MG beyond their desired range of operation. This chapter reviews the control and management techniques to retain the voltage and frequency of such MGs within a predefined safe zone. Suitable real time, corrective, and preventive controllers are discussed on the generation and demand side, which aim to satisfy various objectives at different time instances. First, the necessity of such controllers and mechanisms is explained in both grid-tied and islanded modes and during the transition between these modes. Then, islanding detection and its impact on MG management are briefly discussed. Afterwards, the MG's control architecture is outlined, and the existing approaches in the literature are described briefly. Finally, three case studies on different aspects of MG control are reported to show the applicability and criticality of such services for MG operation. The emphasis of the case studies is on the islanded MG operation because frequency and voltage issues are more pronounced for those types of MGs. In particular, a new generalised droop-based controller is explained in Section 2.4.1 as an example of advanced power-sharing strategies for voltage and frequency regulation with the plug-and-play feature. In Section 2.4.2, the primary frequency control problem is tackled from the demand control perspective, where demand response (DR) resources are altered to provide frequency and voltage regulation within a short period of time. Finally, a corrective and preventive controller is outlined and explained in Section 2.4.3. The corrective controller takes action immediately after the occurrence of an event that violates the voltage or frequency by defining the least cost solution among available options. In the preventive controller, generation and load demand forecast are used to predict unexpected events in very short horizons that can lead to voltage/frequency violations and take suitable actions beforehand.

Energy management system (EMS) in microgrids (MGs) encompasses both dynamic economic dispatch (DED) and dynamic emission dispatch problems, known as dynamic economic emission dispatch (DEED). In this paper, this system is implemented for an MG with different distributed generations. The aim of 24-hour-cycle scheduling is to determine the generation level of diesel generators (DGs), participation rate of responsive loads and their incentives, charging/discharging status of the battery, and the amount of exchanged energy with the main grid. The scheduling task is performed with the objectives of minimizing operation costs and emissions and maximizing the benefit of MG operator (MGO) resulting from the demand response (DR). Due to their interactive operation, the customers’ behavioral model and the relevant cost functions have a significant impact on the optimal operation. In this paper, the cost function of customers is developed in the incentive-based DR program (DRP) with the aim of receiving a more realistic incentive and then it is combined with DEED. Finally, the zero energy balance (ZEB) interaction between the MG and the main grid is proposed as a solution. Regarding the multi-objective and nonlinear optimization problem, the proposed method, with the help of whale optimization algorithm (WOA), is implemented on a small-scale MG in MATLAB software, where electrical loads are also taken into account. Simulation results highlight the fact that the integration of DR into DEED along with import/export ZEB between the MG and the main grid leads to optimal operation and reduced power imported from the main grid, and helps maintain the load/generation balance.

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.

Background: Integrated energy systems face inherent conflicts of interest among various entities, which complicate efficient energy management. Understanding and resolving these conflicts are essential for optimizing the performance and economics of multi-agent integrated energy systems. Objective: This paper aims to analyze energy management strategies in multi-agent integrated energy systems and propose an innovative energy scheduling strategy to enhance system-wide economic performance and operational efficiency. Methods: An in-depth analysis of the operational characteristics of individual microgrids (MG) must be conducted and a demand response model that integrates both electrical and thermal loads should be developed. Ahierarchical master-slave game optimization framework with the system operator as the upper-level leader and microgrid load aggregators, energy storage stations, and wind farms as lower-level followers should be established. Power interaction channels among entities to enable coordinated energy exchange should be designed. At the upper level, an adaptive particle swarm optimization algorithm to determine optimal energy pricing and demand response compensation has to applied. At the lower level, overall operational costs using energy scheduling strategies should be minimized. Results: To validate the proposed method, four scenarios were simulated using the control variable method. Taking Scenario 3 as an example, where energy exchanges between parks were eliminated and each park operated independently, the total profit decreased by 14387 CNY (Chinese Yuan) compared to the proposed master-slave game optimization strategy. This demonstrates the critical role of coordinated energy exchanges in improving system efficiency and highlights the effectiveness of the proposed method. Conclusion: The proposed strategy not only addresses the inherent conflicts in multi-agent integrated energy systems but also enhances system-wide economic and operational efficiency through a robust optimization framework.

Energy management system (EMS) is able to coordinate the generation and load to ensure economic and reliable operation and improve performance metrics of microgrids (MGs). However, most the previous studies have focused mainly on the optimal dispatch of the gas turbines and/or energy storage systems inside a normal MG. Very few works have been carried out to address the potential power outage in MGs. In this study, a novel optimal approach in the EMS based on online forecasts is proposed to minimise the system‐wide outages of MG, specifically through pre‐emptive load shedding. Online distributed generations' output forecasts are updated to predict the state of system and characterise potential upcoming outages. Proper control strategies based on receding horizon methods are introduced in this study. Outage mitigation actions using pre‐emptive load shedding are then performed to avoid power outages or minimise the duration of unavoidable outages. The effectiveness of proposed approach can be demonstrated in the presence of various fluctuations, which can be easily integrated in the EMS for both islanded and grid‐connected MGs.

This paper aims at co-optimizing day-ahead operation schedules of distributed energy resources (DER) in a coordinated microgrids (MG) cluster to enhance resiliency. The proposed model strategically integrates the potential flexibility provided by the DERs in neighboring MGs, while capturing the joint portfolio flexibilities on an hourly basis scheduling scheme. In this context, the proposed optimization model, which is formulated as a mixed-integer linear programming (MILP) problem, minimizes the total operation cost of the MGs in both normal and emergency cases, where the upstream grid might be unavailable in the latter leading the MGs to work in an autonomous mode. In order to reveal the merits of the proposed model, multiple case studies are investigated through the modified IEEE 16-node test feeder, where we decompose the original system to a 6- and 10-node systems denoted by MG 1 and MG 2, respectively.

In this paper, a multi-objective optimization model is proposed to calculate best possible size of energy storage system (ESS). The proposed model is solved utilizing weighted sum method. Positive effects of demand response program (DRP) are considered in the proposed paper. By utilizing the weighted sum method, many various solutions are obtained. Then to select the best possible solution, fuzzy satisfying approach is employed. The proposed multi-objective model includes two conflicting objective functions: 1) the first objective function is minimization of microgrid investment cost as well as operation cost; 2) the second objective function is minimization of loss of load expectation (LOLE). Microgrid includes some local units inside itself which may have some unknown outages and also due to variable and unstable output of renewable units, utilization of ESS is essential to improve stability of microgrid. Impact of DRP implementation is evaluated on microgrid related costs and the results are compared to validate the proposed technique. In order to simulate and model the proposed stochastic ESS optimal sizing problem in a microgrid, a mixed-integer program (MIP) is utilized.

Nowadays, power production, reliability, quality, efficiency and penetration of renewable energy sources are amongst the most important topics in the power systems analysis. The need to obtain optimal power management and economical dispatch are expressed at the same time. The interest in extracting an optimum performance minimizing market clearing price (MCP) for the consumers and provide better utilization of renewable energy sources has been increasing in recent years. Due to necessity of providing energy balance while having the fluctuations in the load demand and non-dispatchable nature of renewable sources, implementing an energy management system (EMS) is of great importance in Microgrids (MG). The appearance of new technologies such as energy storage (ES) has caused increase in the effort to present new and modified optimization methods for power management. Precise prediction of renewable energy sources power generation can only be provided with small anticipation. Hence, for increasing the efficiency of the presented optimization algorithm in large-dimension problems, new methods should be proposed, especially for short-term scheduling. Powerful optimization methods are needed to be applied in such a way to achieve maximum efficiency, enhance the economic dispatch as well as provide the best performance for these systems. Thus, real-time energy management within MG is an important factor for the operators to guarantee optimal and safe operation of the system. The proposed EMS should be able to schedule the MG generation with minimum information shares sent by generation units. To achieve this ability, the present thesis proposes an operational architecture for real time operation (RTO) of a MG operating in both islanding and grid-connected modes. The presented architecture is flexible and could be used for different configurations of MGs in different scenarios. A general formula is also presented to estimate optimum operation strategy, cost optimization plan and the reduction of the consumed electricity combined with applying demand response (DR). The proposed problem is formulated as an optimization problem with nonlinear constraints to minimize the cost related to generation sources and responsive load as well as reducing MCP. Several optimization methods including mixed linear programming, pivot source, imperialist competition, artificial bee colony, particle swarm, ant colony, and gravitational search algorithms are utilized to achieve the specified objectives. The main goal of the thesis is to validate experimentally the design of the real-time energy management system for MGs in both operating modes which is suitable for different size and types of generation resources and storage devices with plug-and-play structure. As a result, this system is capable of adapting itself to changes in the generation and storage assets in real-time, and delivering optimal operation commands to the assets quickly, using a local energy market (LEM) structure based on single side or double side auction. The study is aimed to figure the optimum operation of micro-sources out as well as to decrease the electricity production cost by hourly day-ahead and real time scheduling. Experimental results show the effectiveness of the proposed methods for optimal operation with minimum cost and plug-and-play capability in a MG. Moreover, these algorithms are feasible from computational viewpoints while having many advantages such as reducing the peak consumption, optimal operation and scheduling the generation unit as well as minimizing the electricity generation cost. Furthermore, capabilities such as the system development, reliability and flexibility are also considered in the proposed algorithms. The plug and play capability in real time applications is investigated by using different scenarios.

The distributed generation capabilities of microgrids (MGs) arise as essential assets in enhancing grid resilience. The integration of distributed energy sources, controllable loads, and prosumers necessitates the deployment of potent control and communication synergies. While those synergies transform MGs into cyber-physical systems through information technologies able to sense, control, and actuate local resources and loads, they inadvertently expose MGs to cyber-attack threats. Increasing the security of critical communication and control systems against “black swan” events, i.e., high-impact low-probability cyber-physical incidents, is a major priority for MG operations. Such incidents, if left unabated, can intensify and elicit system dynamics instability, eventually causing outages and system failures. In this article, we develop an integrated approach for multiagent MG systems able to perform the detection of malicious cyber-physical attacks based on subspace methods. We employ the small-signal model of an autonomous/islanded MG and consider different attack models targeting the MG’s secondary frequency control. The attack detector is constructed via identifying the stable kernel representation of the autonomous cyber-physical MG in the attack-free case. We illustrate the impact of the attack models as well as the feasibility of the developed detection method in simulation models of the Canadian urban benchmark distribution system.

With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs.