Blockchain-based approach for mitigating malicious load shedding in microgrids

The integration of information and communication technologies (ICTs) has provided modern smart grids, such as direct current (DC) microgrids (MGs), with various advantages, e.g., the possibility of large-scale integration of renewable energy sources and complex control schemes. This extensive deployment of ICTs, however, makes DC MGs prone to a variety of cyber threats, e.g., false data injection (FDI) attacks. On this basis, this paper develops online detection and mitigation schemes to counter FDI attacks against DC MGs. Initially, in the offline training of the detection scheme, the density-based spatial clustering of applications with noise (DBSCAN) method is used for extracting prescriptive features of DC MG measurements and clustering them in healthy and under-attack operations under several uncertainties in DC MG operations. These features are used to train an adaptive network-based fuzzy inference system (ANFIS) architecture for the online identification of FDI attacks. Afterward, the trained ANFIS is used to detect FDI attacks and obtain the attack vectors benefiting from the real-time measurements estimated by an optimal state observer. Finally, an adaptive model predictive controller (AMPC) is developed to update the power-sharing commands—and mitigate the FDI attack—using the ANFIS’s obtained attack vectors. The effectiveness of the proposed framework is evaluated for several realistic FDI attack scenarios and different uncertainties. To show the scalability of the proposed detection and mitigation methods, a DC MG with eight converters is also studied in the presence of FDI attacks and uncertainties using real-time simulations.

A new cyber-security approach for load frequency control of hybrid interconnected renewable power systems

To enhance the reliability, resiliency and sustainability of a microgrid (MG) in the presence of several distributed energy resources (DERs) and loads, communication systems play a vital role, forming it into a cyber-physical system (CPS). However, integration of information and communication technology (ICT) in the MG architecture exposes MG to potential malicious cyber attacks as well. Similarly, electric vehicles (EVs) also come with a host of many cybersecurity risks. The integration of power, communication and transportation sectors empowers the intruders to exploit the vulnerability and disrupt the operation of multiple sectors. Testbed plays a vital role in the study of the threat landscape and the impact of cyber attacks (such as denial of service (DoS) attacks and false data injection attacks (FDIA)) in such cyber-physical infrastructure. This study would further aid to develop and verify the detection and mitigation schemes against cyber attacks.This work presents a detailed study on the development of real-time (RT) cosimulation testbed for inverter-based MG and RT testbed for EV-charging infrastructure. The MG testbed consists of OP5700 RT simulator to emulate cyber-physical layer through HYPERSIM software; and SEL-3530 Real-Time Automation Controller (RTAC) hardware configured with ACSELERATOR RTAC SEL-5033 software. A human-machine interface (HMI) is further developed in ACSELERATOR Diagram Builder SEL-5035 software for local/remote monitoring and control. Furthermore, communication protocols such as Modbus, sampled measured values (SMVs), generic object-oriented substation event (GOOSE) and distributed network protocol 3 (DNP3) on an Ethernet-based interface were established to link the cyber and physical layers. Furthermore, the testbed for EV-charging infrastructure consists of AC and DC emulators to emulate the respective charging station (AC/DC) and EV. It also constitutes various charging cables supported for different charging protocols such as combined charging system (CCS), GB/T and CHAdeMO. To monitor the message exchanges, charging discovery system (CDS) hardware along with its software is used. Few of the test cases have been presented to demonstrate the ability of these testbeds.

The combination of blockchain technology with machine learning (ML) has emerged as a transformative approach to addressing cybersecurity challenges in microgrids (MG). As these systems become increasingly interconnected and dependent on real-time data transmission, they face growing risks from cyber threats such as false data injection attacks (FDIA), data tampering, denial of service (DoS), and adversarial attacks. This study provides a comprehensive analysis of how blockchain and ML can be integrated to mitigate these vulnerabilities. While ML offers advanced capabilities for anomaly detection, threat prediction, and adaptive response, blockchain’s decentralized, transparent, and secure architecture provides a reliable foundation for data and transaction processing. By combining these technologies, MGs can enhance operational efficiency, safeguard data integrity, and strengthen system resilience. This article reviews recent developments in blockchain and ML applications for MG cybersecurity and highlights key enabling technologies and implementation challenges. Future research directions include the design of hybrid models and improvements in scalability. The findings highlight the potential of blockchain and ML to transform cybersecurity in MGs and support the development of safer, more reliable, and sustainable energy systems.

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

The uncertain nature of natural, man-made, and complex phenomena poses a challenge to microgrid (MG) functioning. In the face of such unexpected events, the power supply to the customer degrades and it becomes necessary to manage the performance of MG components. Therefore, the resilience of MG should be a priority. Resilience prepares the system to handle the operational loss and recover quickly to its pre-disturbance state. It is the ability to adapt to changing conditions, withstand it, and rapidly recover from uncertain natural disasters, man-made interruptions, and complex events termed as high-impact low-probability (HILP) events. MG planning and operation strategy for such HILP events enhances resilience. To analyze this strategy, the uncertain nature of MGs needs to be addressed. However, resilience study can be extended throughout the power system but is more suitable for MGs. It is due to the location of MGs at different terrains that makes it more vulnerable to HILP events. Crisp value of resilience parameter fails to capture the wide range of variations in MG behavior. To incorporate these significant variations, fuzzy-based resilience is required. The fuzzy-based resilience planning and operation is flexible and allows variabilities associated with changing environment. This chapter provides a comprehensive analysis of fuzzy-based resilience assessment for MG planning and operation against windstorm. Weibull wind assessment estimates the maximum likelihood of wind speed distribution in a particular region. Distribution lines are the exposed component during windstorm, so the probability of impacting the MG connectivity is very high. Therefore, this chapter focuses on distribution line fragility. The fragility curve of distribution lines depicts the wind speed-dependent failure probability. The region-specific wind profile of windstorms is mapped to the fragility curve of lines to obtain the time and hazard-dependent operational status. The Monte-Carlo probabilistic assessment measures this disruption status of lines by comparing the failure of lines as a function of weather parameter. To evaluate the influence of uncertain parameters on the operation and planning of MG, fuzzy-based system average interruption frequency index (FSAIFI), fuzzy-based system average interruption duration index (FSAIDI), and fuzzy-based average service availability index (FASAI) are calculated. For MG resilience planning, it is essential to assess the time-varying nature of these indices. The characteristics of these indices are thus assessed using the resilience triangle. It describes the resilience level of a system during each specific phase of the windstorm, which are pre-disturbance, degraded, and restorative stages. This analysis is tested on IEEE 33-bus system. Also, a comparative assessment of the resilience triangle and trapezoid approach for the IEEE 33-bus system is provided. This graphical representation of fuzzy-based performance parameters provides an insight into the impact of uncertainties on the MG under HILP events.

Deep learning for online AC False Data Injection Attack detection in smart grids: An approach using LSTM-Autoencoder

The wide implementation of information and communication technologies (ICT) cause power system operations exposed to cyber-attacks. Meanwhile, the tendency of integrated multi energy vectors has worsened this issue with multiple energy coupled. This paper proposes a two-stage risk-averse mitigation strategy for water-energy systems (WESs), incorporating power, natural gas and water systems against false data injection attacks (FDIA) under water-energy nexus. The FDIA on individual sub-systems is modelled through hampering false data integrity to the systems. An innovative two-stage risk-averse distributionally robust optimization (RA-DRO) is proposed to mitigate uneconomic operation and provides a coordinated optimal load shedding scheme for the nexus system security. A coherent risk measure, Conditional Value-at-Risk is incorporated into the RA-DRO to model risk. A Benders decomposition method is used to solve the original NP-hard RA-DRO problem. Case studies are demonstrated on a WES under water-energy nexus and results show that the effectiveness of the method to mitigate risks from potential FDIA and renewable uncertainties. This research provides WES operators an economic system operation tool by optimally coordinating energy infrastructures and implementing reasonable load shedding to enhance cybersecurity.

The role of energy storage systems in resilience enhancement of health care centers with critical loads

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.

With the growing adoption of electric vehicles (EVs) and advent of bidirectional chargers, EV aggregators, such as charging stations, will become a major player in electricity markets, providing voltage regulation (VR) or other services. We present a novel and practical VR scheme that takes advantage of the charging flexibility of EVs in charging stations that are connected to buses in a distribution grid. This VR scheme relies on real-time measurements, as well as estimates of the distribution system state and regulation capacity of each charging station. We then propose a novel false data injection attack (FDIA) against the VR capacity estimation process that exploits the uncertainty in EV mobility and network conditions. We show the attack vector with the largest expected adverse impact is the solution of a stochastic optimization problem, subject to a constraint that ensures it bypasses bad data detection. We determine this attack vector by solving a sequence of convex quadratically constrained linear programs. The case studies examined in a co-simulation platform, based on two standard test feeders, reveal the vulnerability of the VR capacity estimation process.

Information and communication technologies are being implemented in the power industry to develop smarter power grids termed as cyber-physical power systems. Along with the privileges of modern power networks such as reduced total operation cost, they may be negatively affected by cyberattacks including false data injection (FDI) attacks. As a case in point, an adversary can detour security systems, penetrate into the cyber-physical layer, and manipulate the information leading to security threats. This paper presents the development of a hardware-in-the-loop (HIL) testbed for a lab-scale microgrid (MG) located at Southern Illinois University Carbondale to study FDI cyberattacks and the corresponding imposed impacts on the operation of the MG. The proposed framework is established on an OPAL-RT platform, which is a real-time simulator incorporating software/hardware-based simulations, simultaneously. The developed HIL setup comprises data acquisition unit, controllers, and different analog/digital inputs/outputs. The MG contains wind turbines and photovoltaic modules and is able to operate in grid-tied and standalone modalities. The experimental results validate the effects of FDI cyberattacks on the lab-scale MG and verify the resilience of the targeted MG in different scenarios.

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.

A3D: Attention-based auto-encoder anomaly detector for false data injection attacks

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.