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This paper presents a security-constrained unit commitment (SCUC) algorithm which takes into account the intermittency and volatility of wind power generation. The UC problem is solved in the master problem with the forecasted intermittent wind power generation. Next, possible scenarios are simulated for representing the wind power volatility. The initial dispatch is checked in the subproblem and generation redispatch is considered for satisfying the hourly volatility of wind power in simulated scenarios. If the redispatch fails to mitigate violations, Benders cuts are created and added to the master problem to revise the commitment solution. The iterative process between the commitment problem and the feasibility check subproblem will continue until simulated wind power scenarios can be accommodated by redispatch. Numerical simulations indicate the effectiveness of the proposed SCUC algorithm for managing the security of power system operation by taking into account the intermittency and volatility of wind power generation.

This paper presents a stochastic model for the long-term solution of security-constrained unit commitment (SCUC). The proposed approach could be used by vertically integrated utilities as well as the ISOs in electricity markets. In this model, random disturbances, such as outages of generation units and transmission lines as well as load forecasting inaccuracies, are modeled as scenario trees using the Monte Carlo simulation method. For dual optimization, coupling constraints among scenarios are relaxed and the optimization problem is decomposed into deterministic long-term SCUC subproblems. For each deterministic long-term SCUC, resource constraints represent fuel and emission constraints (in the case of vertically integrated utilities) and energy constraints (in the case of electricity markets). Lagrangian relaxation is used to decompose subproblems with long-term SCUC into tractable short-term MIP-based SCUC subproblems without resource constraints. Accordingly, penalty prices (Lagrangian multipliers) are signals to coordinate the master problem and small-scale subproblems. Computational requirements for solving scenario-based optimization models depend on the number of scenarios in which the objective is to minimize the weighted-average generation cost over the entire scenario tree. In large scale applications, the scenario reduction method is introduced for enhancing a tradeoff between calculation speed and accuracy of long-term SCUC solution. Numerical simulations indicate the effectiveness of the proposed approach for solving the stochastic security-constrained unit commitment

This paper presents an optimal expansion planning model for an energy hub with multiple energy systems. Energy hub represents a coupling among various energy infrastructures for supplying electricity, natural gas, and heating loads. Combined heat and power (CHP) and natural gas furnaces are considered within the energy hub to convert energy into other forms. The multiple energy system planning problem would optimally determine appropriate investment candidates for generating units, transmission lines, natural gas furnaces, and CHPs that satisfy electricity and heating load forecasts and hub system constraints. The system performances associated with reliability, energy efficiency, and emission matrices is evaluated for the identified planning schedules. Numerical simulations demonstrate the effectiveness of the proposed multiple energy system expansion planning approach based on energy hub.

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verified.

In this paper, a community microgrid with multiple ac and dc microgrids is introduced and analyzed. Individual microgrids with different frequency and voltage requirements would operate as self-controlled entities, which could also cooperate with neighboring microgrids for providing back-up operations in the community microgrid. A hierarchical coordination strategy with primary, secondary, and tertiary coordination is proposed for the economic operation of an islanded community microgrid. The hierarchical strategy is also applied to a grid-connected community microgrid and the results are discussed. The simulation results verify that the proposed hierarchical coordination strategy is an effective and efficient way for coordinating microgrid flows in an islanded community microgrid, while maintaining the rated frequency and voltage with each microgrid. The simulation results also demonstrate the economic operation of a grid-connected community microgrid in which individual microgrids operate as autonomous agents, while satisfying the community objectives.