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Existing modeling approaches for long-duration energy storage (LDES) are often based either on an oversimplified representation of power system operations or limited representation of storage technologies, e.g., evaluation of only a single application. This manuscript presents an overview of the challenges of modeling LDES technologies, as well as a discussion regarding the capabilities and limitations of existing approaches. We used two test power systems with high shares of both solar photovoltaics- and wind (70% - 90% annual variable renewable energy shares) to assess LDES dispatch approaches. Our results estimate that better dispatch modeling of LDES could increase the associated operational value by 4% - 14% and increase the standard capacity credit by 14% - 34%. Thus, a better LDES dispatch could represent significant cost saving opportunities for electric utilities and system operators. In addition, existing LDES dispatch modeling approaches were tested in terms of both improved system value (e.g., based on production cost and standard capacity credit) and scalability (e.g., based on central processing unit time and peak memory usage). Both copper plate and nodal representations of the power system were considered. Although the end volume target dispatch approach, i.e., based on mid-term scheduling, showed promising performance in terms of both improved system value and scalability, there is a need for robust and scalable dispatch approaches for LDES in transmission-constrained electric grids. Moreover, more research is required to better understand the optimal operation of LDES considering extreme climate/weather events, reliability applications, and power system operational uncertainties. Comment: 45 pages, 16 figures, Submitted to Renewable and Sustainable Energy Reviews

Coal power plants annually generate quantities of byproducts that release environmentally hazardous heavy metals like Cd and Pb. Understanding the behavior and spatiotemporal impacts on soils of these releases is crucial for pollution control. This study investigated the concentrations and isotope ratios of Cd/Pb in combustion byproducts, depositions and soils collected from a coal-fired power plant or its surrounding area. The pulverized fuel ash (PFA) and desulfurized gypsum (DG) exhibited heavier Cd isotopes with Δ114Cd values of 0.304‰ and 0.269‰, respectively, while bottom ash (BA) showed lighter Cd isotopes (Δ114CdBA-coal = -0.078‰), compared to feed coal. We proposed a two-stage condensation process that governs the distribution of Cd/Pb, including accumulation on PFA and DG within electrostatic precipitators and desulfurization unit, as well as condensation onto fine particles upon release from the stack. Emissions from combustion and large-scale transport make a significant contribution to deposition, while the dispersion of Cd/Pb in deposition is primarily influenced by the prevailing wind patterns. However, the distribution of Cd/Pb in soils not only exhibit predominant wind control but is also potentially influenced by the resuspension of long-term storage byproducts. The power plant significantly contributes to soil in the NW-N-NE directions, even at a considerable distance (66%-79%), demonstrating its pervasive impact on remote regions along these orientations. Additionally, based on the vertical behavior in the profile, we have identified that Cd tends to migrate downward through leaching, while variations in Pb respond to the historical progression of dust removal.

Optimally scheduling multi-energy flow is an effective method to utilize renewable energy sources (RES) and improve the stability and economy of integrated energy systems (IES). However, the stable demand-supply of IES faces challenges from uncertainties that arise from RES and loads, as well as the increasing impact of cyber-attacks with advanced information and communication technologies adoption. To address these challenges, this paper proposes an innovative model-free resilience scheduling method based on state-adversarial deep reinforcement learning (DRL) for integrated demand response (IDR)-enabled IES. The proposed method designs an IDR program to explore the interaction ability of electricity-gas-heat flexible loads. Additionally, the state-adversarial Markov decision process (SA-MDP) model characterizes the energy scheduling problem of IES under cyber-attack, incorporating cyber-attacks as adversaries directly into the scheduling process. The state-adversarial soft actor-critic (SA-SAC) algorithm is proposed to mitigate the impact of cyber-attacks on the scheduling strategy, integrating adversarial training into the learning process to against cyber-attacks. Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources. Moreover, the proposed method demonstrates resilience against cyber-attacks. Compared to the original soft actor-critic (SAC) algorithm, it achieves a 10% improvement in economic performance under cyber-attack scenarios. Accepted by Applied Energy, Manuscript ID: APEN-D-24-03080