For distributed solid electricity thermal storage aggregators (DSETSA), the uncertainty of the marginal clearing price may lead to the problem of multibidding scenarios (including successful, part successful, and failed biddings) in the electricity spot market. Moreover, the marginal operating cost affecting the bidding revenue in the spot market is not considered in the existing methods, which challenge the bidding of the aggregators. To address the challenge, this article proposes an optimized operation framework for DSETSA. First, based on the incomplete information characteristics of the spot market, an optimal bidding model which incorporates marginal operating cost constraints for DSETSA under multibidding scenarios is proposed to increase the operation profit. Second, the DSETSA's multibidding scenario problem induced by the uncertainty of the marginal clearing price in the electricity spot market is cast into a probability distribution representation using the Bayesian incomplete information theory to increase the chances of winning bids. Finally, framework establishes the relationship between the bidding price, electricity demand, and public traded electricity in the spot market. The effectiveness of the proposed framework is demonstrated through simulations.

The deployment of microphasor measurement units, supervisory control and data acquisition systems, and smart meters has revolutionized distribution systems, moving toward more intelligence and facilitating state estimation. However, the asynchronous nature and varying sample rates of the measurements raise a significant challenge to state estimation in low-observable distribution systems. This article proposes a novel data-driven framework for state estimation in low-observable distribution systems. The framework incorporates super-resolution imputation techniques that are capable of handling multitime scale measurements. Specifically, the proposed framework utilizes Wasserstein divergence conditional adversarial networks to implement multivariate super-resolution imputation. This involves employing a gated recurrent unit generator with additive attention to impute super-resolution measurements, as well as a discriminator with self-attention to approximate the Wasserstein divergence between the imputed values and the ground truth. Furthermore, a tailor-made physical-guided bilinear neural network is employed to estimate the operational state of low-observable distribution systems by leveraging matrix factorization techniques to capture the low-rank characteristics. Simulation results illustrate the superiority of the proposed approach from the standpoint of multitimescale measurements imputation and state estimation in low-observable distribution systems.

In the transition to a society with net-zero carbon emissions, high penetration of distributed renewable power generation and large-scale electrification of transportation and heat are driving the conventional distribution network operators (DNOs) to evolve into distribution system operators (DSOs) that manage distribution networks in a more active and flexible way. As a radical decentralized data management technology, distributed ledger technology (DLT) has the potential to support a trustworthy digital infrastructure facilitating the DNO–DSO transition. Based on a comprehensive review of worldwide research and practice, as well as the engagement of relevant industrial experts, the application of DLT in distribution networks is identified and analyzed in this article. The DLT features and DSO needs are first summarized, and the mapping relationship between them is identified. Detailed DSO functions are identified and classified into five categories (i.e., “planning,” “operation,” “market,” “asset,” and “connection”) with the potential of applying DLT to various DSO functions assessed. Finally, the development of seven key DSO functions with high DLT potential is analyzed and discussed from the technical, legal, and social perspectives, including peer-to-peer energy trading, flexibility market facilitation, electric vehicle charging, network pricing, distributed generation register, data access, and investment planning.

Owners of combined heat and power (CHP), e.g., industrial manufacturers, are motivated to provide frequency response to power grids due to clear financial benefits. Yet, the slow response speed of CHP limits its capability in providing such services. Moreover, frequent adjustments would cause a faster lifetime reduction of CHP and rapid pressure fluctuation in the gas network. To further unlock the flexibility of CHP, this article integrates a battery unit with CHP via a power electronic interface. A filter-based coordinative controller is designed for smoothing short-term fluctuations in CHP outputs. Based on the filter parameters and frequency response requirements, the minimum required capacity of the battery is identified. The results show that the proposed system enhances the capability of CHP for frequency response and mitigates the associated adverse effects on the gas network. The required capacity of the battery is economically feasible considering the benefit it brings to the CHP.

In this paper, an intelligent energy management framework with demand response capability was proposed for industrial facilities. The framework consists of multiple components, including industrial processes modeled by the state task network method, thermostatically controlled loads, like the heating, ventilation, and air conditioning system with chilled water storage, renewable generation, like photovoltaic arrays and electric vehicles. These components are first modeled and their operation is then optimized in time-of-use pricing schemes. Factors that affect several components at the same time, e.g., the number of workers, are considered. The optimization is formulated as a mixed integer linear programming problem. A general tire manufacturing facility was investigated as the case study. Simulation results show that the proposed intelligent industrial energy management with demand response is able to effectively utilize the flexibility contained in all parts of the facility and reduce the electricity costs, as well as the peak demand of the facility, while satisfying all the operating constraints.

Increasing penetration of renewable generation results in lower inertia of electric power systems. To maintain the system frequency, system operators have been designing innovative frequency response products. Enhanced frequency response (EFR) newly introduced in the UK is an example with higher technical requirements and customized specifications for assets with energy storage capability. In this paper, a method was proposed to estimate the EFR capacity of a population of industrial heating loads, bitumen tanks, and a decentralized control scheme was devised to enable them to deliver EFR. Case study was conducted using real UK frequency data and practical tank parameters. Results showed that bitumen tanks delivered high-quality service when providing service-1-type EFR, but underperformed for service-2-type EFR with much narrower deadband. Bitumen tanks performed well in both high- and low-frequency scenarios, and had better performance with significantly larger numbers of tanks or in months with higher power system inertia.