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A risk-aware scheduling method of multienergy virtual power plant (MEVPP) is proposed to measure the uncertainty in MEVPP. First, a novel day-ahead uncertainty scenario generation method based on denoising diffusion probabilistic model is proposed, and historical data are employed to learn the error relationship between real power curves and predict power curves. The probability distribution of the prediction error which describes the day-ahead output power curve of renewable energy source is learned by parameter training. Subsequently, the effect of risk aversion on decision-making is investigated by implementing conditional value-at-risk in the optimization model, MEVPP operation mode under the carbon trading and green certificate trading mechanism is analyzed. Finally, the proposed scheme is implemented on a test MEVPP with carbon trading, and green certificate trading is addressed in detail through a numerical study. Moreover, the effects of the operator's risk-averse behavior on the MEVPP are investigated. [ABSTRACT FROM AUTHOR]

A two-stage robust planning method for energy storage in distribution networks based on load prediction is proposed to address the uncertainty of active load in energy storage planning. First, considering the uncertainty of active load, a short-term load forecasting model combining the mutual information method and BiLSTM is established based on k-means++ clustering. Second, based on the results of load forecasting, a comprehensive norm-constrained uncertainty set is constructed, and a two-stage robust model for distribution network energy storage planning is established. The first stage aims to minimize the annual investment cost of the energy storage system, while the second stage aims to minimize the daily operating cost of the distribution network. At the same time, a second-order cone relaxation transformation model with non-convex constraints is introduced to ultimately achieve the optimal economy of the distribution network in energy storage planning. Finally, the effectiveness of the proposed method and model is validated on the IEEE 33-node distribution network model using the MATLAB platform. [ABSTRACT FROM AUTHOR]

This paper is concerned with optimal security problem of cyber‐physical systems with state attacks. Consider a linear physical system, assuming that the control input signal of the network layer is vulnerable to state attack. For a system that cannot measure all states, by combining output integral sliding mode, robust observer, and adaptive methods, an adaptive output integral sliding mode control method is proposed to maintain the safe operation of cyber physical systems under state attacks. Moreover, by optimizing the linear control gain matrix, the control strategy the authors designed can use the minimum cost to compensate the impact on the CPS system. Different from the existing results, (1) It is assumed that the system information of the attack signal is not fully known, we assume that the attacker can only measure a small amount of state information. (2) Not only the stability of ideal sliding mode is proved, but also the upper bound of the augmented state composed of system states and the error dynamic is given. (3) The sliding mode compensator can eliminate the impact of state attack, on the one hand, the damage caused by the state attack has been eliminated by using the upper bound of error, and on the other hand, the cost is reduced by optimizing the linear control gain. Finally, a power system with 3 generators and 6 buses is used to prove the effectiveness of the adaptive output integral sliding mode control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the problem of attack detection for actuator attacks and power balance on cyber physical power systems (CPPSs) is investigated. An economic optimization strategy based on observer data is proposed to enable optimal power balance of the attacked system. System states and cyber attacks are estimated by a sliding mode observer satisfying the H∞$H_{\infty }$ performance index. Then, the system power is redistributed according to the optimal economic dispatch. Finally, several simulation cases are presented to illustrate the effectiveness of attack detection and economic optimization strategy. [ABSTRACT FROM AUTHOR]