With the increasing development of smart grid, multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation (SMPC) with secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.

Preventive transient stability control is an effective measure for the power system to withstand high-probability severe contingencies. It is mathematically an optimal power flow problem with transient stability constraints. Due to the constraints involved for differential algebraic equations of transient stability, it is difficult and time-consuming to solve this problem. To address these issues, this paper presents a novel deep reinforcement learning (DRL) framework for preventive transient stability control of power systems. A distributed deep deterministic policy gradient is utilized to train a DRL agent that can learn its control policy through massive interactions with a grid simulator. Once properly trained, the DRL agent can instantaneously provide effective strategies to adjust the system to a safe operating position with a near-optimal operational cost. The effectiveness of the proposed method is verified through numerical experiments conducted on a New England 39-bus system and NPCC 140-bus system.

The growing penetration of electric vehicles (EVs) and the popularity of fast charging stations (FCSs) have greatly strengthened the coupling of the urban power network (PN) and traffic network (TN). In this paper, a potential security threat of the PN-TN coupling is revealed. Different from traditional loads, a regional FCS outage can lead to both the spatial and temporal redistribution of EV charging loads due to EV mobility, which further leads to a power flow redistribution. To assess the resulting potential threats, an integrated PN-TN modeling framework is developed, where the PN is described by a direct current optimal power flow model, and the TN is depicted by an energy-constraint traffic assignment problem. To protect the privacy of the two networks, an FCS outage distribution factor is proposed to describe the spatial-temporal redistribution ratio of the charging load among the remaining FCSs. Moreover, to protect the security of the coupled networks, a price-based preventive regulation method, based on the spatial demand elasticity of the EV charging load, is developed to reallocate the charging load as a solution for insecure situations. Numerical simulation results validate the existence of the PN-TN coupling threat and demonstrate the effectiveness of the regulation method to exploit the spatial flexibility of EV loads.