The power trading model considering operating agents at different levels, such as distribution utility, microgrid operators, and end-users allows for efficient energy resource coordination and effective utilization. The simultaneous and hierarchical optimization of multiple agents has not yet been attempted. The techno-economic aspects can be accomplished more effectively if the energy management framework considers hierarchically coordinated decision-making of all agents. The decisions of all agents are interlinked and can be realized with a hierarchical Stackelberg game model. This article proposes an energy management framework incorporating a three-level hierarchical decision approach, through which multiple operating agents can actively participate in energy management to achieve their respective goals. In this framework, a game-theoretic dynamic pricing scheme is used to enable the interaction of distribution utility and microgrid operators as well as the microgrid operators and end-user aggregators. This arrangement enables end-user aggregators to negotiate adequately with microgrid operators. This article also investigates the impact of risk-averse and risk-seeker decisions of microgrid operators on the operating cost of distribution utility. The numerical results establish the effectiveness of the proposed framework and demonstrate that the proposed participatory strategy can improve economic benefits with technical aspects, such as lower peak demand and improved voltage profiles.

The decentralized economic scheduling of multimicrogrid is an important aspect in the operational planning of microgrids (MGs). This article proposes an approach to maximize economic benefit among MGs through cooperative scheduling. The cooperative scheduling is achieved via price signals so that MGs are encouraged to share power among themselves for economic benefit. An MG operator generates a time-variable tariff based on energy trading status so that the parking lot operator and distributed battery energy storage system aggregator participate with flexibility in the MG’s energy management. The Shapley value method is used for generating fair price signals. The stochastic Dantzig–Wolfe decomposition is used to solve the resulting optimization problem in a decentralized manner. The uncertainties related to load demand and renewable energy sources are captured using scenario-based methods, whereas the uncertainty associated with plug-in hybrid electric vehicles is modeled using copula theory based estimation. The simulation studies and comparison with the existing methods establish that the proposed approach effectively reduces the total energy cost in a decentralized manner with the minimum amount of information exchange.

A resilient optimal energy scheduling in interconnected smart buildings considering cyber-attack detection has been proposed in the present work. The proposed resilient scheduling considers interconnected multiple smart buildings with power exchange capability among the smart buildings. It is considered that each smart building is equipped with different types of distributed energy resources, battery storage systems, combined heat and power generators, thermal storage systems, and smart appliances. A comprehensive optimal and robust scheduling is formulated considering false data injection attack. The proposed method uses the information of anomaly between the actual and forecasted bills for detecting the cyber attack and making a resilient scheduling against possible attacks. The studies indicate that the optimal scheduling in interconnected smart buildings may reduce the cost up to 4.53% for the system depending on number of interconnected smart buildings.

State estimation algorithms play a crucial role in monitoring the modern grid. Operators employ state estimation algorithms to determine the current prevailing conditions of the grid. Recently false data injection attacks have shown their detrimental effects on the state estimation techniques by bypassing the standard statistical bad data detection algorithm. Such kind of an attack vector injection is possible when the attacker can possibly access the meters or the communication channels and has sufficient resources to modify the measurement data. This work showcases the real time attack vector implementation using OPNET followed by its effective diminution using the data diode scheme. Using such test-bed it can be shown that the proposed data diode scheme can not only prevent the injection of attack vectors in the communication channels but also can effectively mitigate the effects of false data intrusion on the grid. Although such schemes are costly, still it shows a potential physical defending policy of the meters from such genres of attack. An extensive analysis on the standard IEEE 14 bus test system provides a clear insight regarding such aforementioned propositions.

Modern smart home energy management system (SHEMS) is naturally prone to cyber attack, hence it demands cyber attack resilient scheduling schemes. Current scenario of SHEMS may result in increased charging and discharging cycles deteriorating the battery life. Therefore, demand scheduling formulations also need to cater the effect of battery degradation cost along with user comfort. The present work attempts to formulate a comprehensive scheduling problem in terms of energy cost minimization considering the battery degradation cost. Further, a cyber attack resilient scheduling model is proposed in this study. This article investigates the effect of demand scheduling on the life span of battery as well as the energy cost. Further, false data injection attack (FDIA) has been modeled using machine learning techniques, and its effects on the scheduling has also been incorporated in the objective function. Scenario tree based stochastic bill generation has been also formulated to develop an FDIA resilient scheduling. Optimisation results of the study have established that the resulting formulation is robust against FDI attacks.