This article proposes a coordinated operation strategy for a virtual power plant (VPP) with multiple DER aggregators under the wholesale energy and regulation service market. Considering the market clearing process, VPP operation profit, and DER aggregators’ interests, a tri-layer hierarchical framework for VPP is developed to formulate the bidding plans to the superordinate market operator and the dynamic price incentive curves for the subordinate DER owners cooperatively. By using the discrete price quota curves, big-M method, KKT optimal conditions, and strong duality theorem, the tri-layer problem is transformed into an equivalent and tractable mixed integer linear programming problem. Case study with the 141-bus system is conducted to validate the effectiveness of the proposed approach. The advantages of lower cost with operation security guarantee and higher flexibility are demonstrated by comparing with other models.

Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors.

This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction and data marking. The negative abnormal points, whose wind speed are greater than cut-in speed and power are below zero, are first filtered in the data pre-cleaning step. The scatter figure of the rest wind power data forms the WPC image and corresponding binary image. In the normal data extraction step, the principle part of the WPC binary image, representing the normal data, is extracted by the mathematical morphology operation (MMO). The optimal parameter setting of MMO is determined by minimizing the dissimilarity between the extracted principle part and the reference WPC image based on Hu moments. In the data mark step, the pixel points of scattered and stacked abnormal data are successively identified. The mapping relationship between the wind power points and image pixel points is built to mark the wind turbine normal and abnormal data. The proposed image-based algorithm is compared with k-means, local outlier factor (LOF), combined algorithm based on change point grouping algorithm and quartile algorithm (CA). Numerous experiments based on 33 wind turbines from two wind farms are conducted to validate the effectiveness, efficiency and universality of the proposed method.

Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between carbon emission and power allocation. So this article leverages the high approximation capability and the mixed-integer linear programming (MILP) representability of the deep neural networks to tackle the complex CEF model in carbon-electricity coordinated optimization. The compact constraint learning approach is proposed to learn the mapping from power injection to bus emission with sparse neural networks (SNNs). Then the trained SNNs are transformed equivalently as MILP constraints in the downstream optimization. In light of the “high emission with high price” principle, the blocked carbon price mechanism is designed to price emissions from the demand side. Based on the constraint learning and mechanism design, this article proposes the carbon-aware energy management model in the tractable MILP form to unlock the carbon reduction potential from the demand side. The case study verifies the approximation accuracy and sparsity of SNN with fewer parameters for accelerating optimization solution and reduction effectiveness of demand-side capability for mitigating emission.

This paper proposes a multiagent-based bilevel operation framework for low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the load agents optimize the regulation signals for various types of loads to maximize profits; in the lower level, the distribution network operator makes optimal dispatching decisions to minimize the operational costs and calculates the distribution locational marginal price and carbon intensity. The distributed flexible load agent has only incomplete information on the distribution network and cooperates with other agents using networked communication. Finally, the problem is formulated as a networked multiagent constrained Markov decision process, and it is solved using a safe reinforcement learning algorithm called consensus multiagent constrained policy optimization, considering the carbon emission allowance for each agent. Case studies with the IEEE 33-bus, 123-bus and practical 1031-bus distribution network systems demonstrate the effectiveness of the proposed approach in terms of satisfying the carbon emission constraint on the demand side and ensuring the safe operation of the distribution network without complete information exchange.

Public Safety Power Shutoffs (PSPS), also known as proactive de-energizations, proactively de-energize a portion of power systems to mitigate the risk of catastrophic wildfires caused by electric infrastructure. Since the first practice in Southern California in 2012, PSPS have been widely discussed in government and industry, but seldom in the academic literature. This paper surveys the PSPS program in California, including its history, policies, and practices. In practice, PSPS present strong interactions between electric utilities and customers, yielding PSPS a trade-off problem to balance the risk of power-line-ignited wild-fires (i.e., wildfire risk) against the harms of power shutoff (i.e., PSPS risk). In this regard, this paper summarizes the industry-standard and research methods for PSPS studies, including models, data sources, and test systems. It is suggested to integrate engineering solutions with social-economic science, such as energy innovation, energy equity, and PSPS uncertainties, in future PSPS studies.