Integration of electricity and heat distribution networks offers extra flexibility to system operation and improves energy efficiency. The energy hub (EH) plays an important role in energy production, conversion and storage in such coupled infrastructures. This paper provides a new outlook and thorough mathematical tool for studying the integrated energy system from a deregulated market perspective. A mathematic program with equilibrium constraints (MPEC) model is proposed to study the strategic behaviors of a profit-driven energy hub in the electricity market and heating market under the background of energy system integration. In the upper level, the EH submits bids of prices and quantities to a distribution power market and a heating market; in the lower level, the two markets are cleared and energy contracts between the EH and two energy markets are determined. Network constraints of physical systems are explicitly represented by an optimal power flow problem and an optimal thermal flow problem. The proposed MPEC formulation is approximated by a mixed-integer linear program via performing integer disjunctions on the complementarity and slackness conditions and binary expansion technique on the bilinear product terms. Case studies demonstrate the effectiveness of the proposed model and method.

Modern energy systems are experiencing the transition towards renewable-powered ones. Some conventional thermal units based on synchronous machines are gradually decommissioned and replaced by power electronics interfaced renewables. Thus, the lack of natural inertia and governor damping, which are the features of synchronous machines, raises significant concern about system frequency stability, including the faster rate of change and lower nadir point of frequency. Meanwhile, with the rapid development of communication and Internet of Things technologies, distributed energy resources can be aggregated as a virtual power plant to help balance real-time electricity demand and supply. However, the capability of utilizing the whole virtual power plant to provide adjustable inertia support has not been explored yet. In this paper, we propose a framework of the synchronous virtual power plant based on grid-forming inverter interfaced distributed energy resources. By coordinating the parameter settings of grid-forming inverters, the virtual power plant provides inertia support. Also, we design an online learning-based parameter settings method that makes the inertia of the virtual power plant adjustable. A case study in IEEE 34 nodes system illustrates the effectiveness of the proposed method.

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.

To quantify the cost and low-carbon competitiveness of electrolytic hydrogen in China, this paper presents a detailed assessment of the levelized cost of electrolytic hydrogen produced by a photovoltaic and grid-based hydrogen system (PGHS). This assessment considers the provincial differences in electricity prices, shares of renewables in power grids, and solar radiations in China. We find that in 2019, the levelized cost of hydrogen (LCOH₂) in 31 provinces varies from 31.5 ¥ per kg to 46.8 ¥ per kg in the single electricity price scenario, and reduces by up to 5.5 ¥ per kg when implementing a time-of-use price. In terms of CO₂ emissions, we demonstrate that electrolytic hydrogen has greater CO₂ emissions than the widespread coal-based hydrogen in 21 out of 31 provinces of China. The results suggest that implementing time-of-use electricity prices and subsidizing photovoltaics, instead of electricity prices and electrolyzers, can improve the performance of electric hydrogen production in terms of cost and CO₂ emissions. Further provincial analysis considering hydrogen demand potentials shows that, under stricter CO₂ emission constraints, substituting fossil fuel with electrolytic hydrogen can reduce CO₂ emissions. However, its popularization is impeded by fossil fuels with low costs. Due to the regional differentiation in the cost competitiveness of electrolytic hydrogen, different types and levels of subsidies should be carefully adopted depending on the provincial characteristics in electricity prices, solar radiations, renewable integrations, fossil fuel prices, and hydrogen demand potentials. In the end, when comparing seven pathways towards electrolytic hydrogen development from 2020 to 2050, we find that the production cost of the electrolytic hydrogen is unbundled from the restriction of CO₂ emission requirements after 2030, and a high carbon price may accelerate the cost competitiveness of electrolytic hydrogen by decades.

With the advance in communication facilities and information technologies, the state estimation (SE) of distribution networks is subject to intensified cybersecurity threats caused by false data injection attacks (FDIAs). To address the issues, this paper proposes a novel FDIA detection strategy for unbalanced distribution networks. The SE model and corresponding general imperfect FDIA are introduced first to emulate the attacking behavior in practice. To achieve FDIA detection, we propose a square-root unscented Kalman filter (SR-UKF) based forecasting-aided SE (FASE) to generate estimation results. By modifying the filtering step of the proposed FASE into a redundant linear regression form, random outliers can be effectively detected and suppressed by leveraging the projection statistics (PS). Afterward, based on the acquired SE results, a generalized likelihood ratio test (GLRT) is designed to detect FDIAs on consecutive snapshots. In the GLRT, the dynamic time warping (DTW) distance between two innovation sequences is set as the test variable, which is compared with the offline determined detection threshold under a specific false alarm rate. The feasibility of the proposed general imperfect FDIA and the effectiveness of the proposed FDIA detection strategy are validated through extensive numerical simulations.