Recently, the construction of renewable energy generation systems in uninhabited areas such as deserts and oceans has increased significantly, along with the low-carbon transition of the global energy system. However, the traditional planning methods that rely on information from nearby meteorological stations often yield suboptimal planning schemes due to the vastness of the uninhabited areas and significant meteorological spatiotemporal differences. Therefore, an information value-driven comprehensive benefit evaluation method is proposed in this article to efficiently and accurately evaluate suitable locations for renewable power generation system. The information matrix of geo-meteorological coupling is constructed through the numerical weather prediction correction method of Fourier ForeCasting Neural Network. To replace the traditional time-consuming planning method based on traversal optimization, a comprehensive evaluation method that fully considers the value of information itself and its multi-dimensional values such as economy, safety, and low-carbon for the planning scheme is proposed in this paper. Case study based on the Tengger Desert in China is carried out to verify the efficiency of the proposed method.

Advancements in information technology and the exponential growth of data in energy systems present significant potential for intelligent and secure grid operations. However, variability in data quality remains a critical constraint. To address the lack of focus on the role of high-quality data in improving decision-making, this paper proposes a two-stage data purification framework. The first stage employs reinforcement learning-based valuation with refined policy strategies to quantify data quality, providing ranked references for decision-focused filtering in the second stage. Applied to stochastic optimization in wind-integrated unit commitment, the proposed method demonstrates its effectiveness on IEEE 30 and IEEE 118-bus systems by accurately identifying high-quality data and achieving economic benefits under varying uncertainty levels. This work aims to introduce a conceptual framework for data-centric decision-making improvement and provide methodological guidance for both academia and industry.

Currently, the rapid development of Internet of Things (IoT) technology is promoting the development of smart grids. However, because of the numerous loads present and the surge of data in smart grids, network congestion, transmission delay, and insufficient channel resources pose pressing challenges for accurate, real-time transmission in load control communication systems. Mobile edge computing (MEC) and nonorthogonal multiple access (NOMA) technology, as new types of communication architectures, can significantly improve the communication quality in such network system. In this article, we first establish an NOMA-MEC system model with relay assistance based on cloud-edge collaboration in smart grids, aiming to minimize the energy consumption cost of the system while satisfying constraints on relay power and computation latency. In addition, we formulate a powerful four-slot transmission strategy to support cooperative computation and communication in the proposed NOMA-MEC system with relay assistance. Since the optimization problem is nonconvex, an efficient joint cooperative computation and communication algorithm with relay assistance is developed to solve it. Numerical results show that the proposed resource allocation strategy can significantly improve the communication transmission quality and reduce the energy consumption cost of the system.

Battery storage is a key technology for distributed renewable energy integration. Wider applications of battery storage systems call for smarter and more flexible deployment models to improve their economic viability. Here we propose a hybrid energy storage system (HESS) model that flexibly coordinates both portable energy storage systems (PESSs) and stationary energy storage systems (SESSs) in a grid. PESSs are batteries and power conversion systems loaded on vehicles that travel between grid nodes with price differences to alleviate grid congestion. PESSs can charge/discharge at grid nodes or swap (part of) batteries with SESSs for profit maximization. We introduce a spatiotemporal decision-making framework for HESS including the planning of SESS and the on-demand dispatch of PESS. We propose a two-phase decision-making algorithm (TPDM), where the first phase uses a spatiotemporal cost-effectiveness aggregation method to determine the optimal SESS location; the second phase shapes a low-complexity solution space by arc destroying and repairing. The results show that HESS achieves significant arbitrage benefit improvement in 86.3% of the operating periods through a year compared with SESS and PESS alone. Compared with commercial solver, the proposed TPDM, on average, can reduce the computational time by 95.5% with an optimality of 1.04%.Note to Practitioners—Battery storage and electric vehicles (EVs) play a crucial role in renewable energy integration and in shaping a low-carbon and sustainable energy and transportation systems. To achieve efficient and scalable management of battery storage across energy and transportation systems, we incorporate the portable energy storage (i.e., batteries transported by vehicles) and stationary energy storage (i.e., batteries placed at grids), into a hybrid energy storage system (HESS), and develop efficient planning framework and scheduling algorithms. Specifically, the proposed methods can provide decision supports for the owners of battery assets to determine the optimal SESS location and for the high-quality coordination of battery charging, swapping, and routing in a HESS. Our methods also have potentials in the on-demand applications of battery storage and EVs across energy and transportation systems, such as ancillary services, grid investment deferral, and battery trading and sharing.

In Part I of this two-part article series, we proposed the locational marginal hydrogen price (LMHP) decomposition theorem and derived analytical expressions for LMHP components. Notably, the application of the LMHP decomposition theorem is based on the premise of obtaining optimal solution for the hydrogen market. Due to the existence of the Weymouth equation, which is used to characterize pipeline hydrogen flow, the hydrogen market clearing model is strongly nonconvex and difficult to solve directly. Although the literature has investigated solution algorithms for the Weymouth equation, improving the calculation efficiency while ensuring the accuracy of the solution remains a challenge. To address this knowledge gap, Part II of this two-part article series proposes an improved second-order cone programming (SOCP) algorithm with umbrella constraint identification to solve the hydrogen market. The hydrogen market clearing model is transformed into a mixed-integer SOCP problem, which can be solved iteratively. Before iteration, redundant constraints are removed to minimize the representation of the hydrogen market clearing model, thereby improving computational efficiency. Case studies based on the Belgium-20 node system and a 90-node system verify the effectiveness of the proposed LMHP decomposition theorem and solution algorithm for the hydrogen market.

The rapid development of the Internet of Things (IoT) has given rise to a novel business model, i.e., Internet of Storage (IoS), in which distributed in-home storage systems can be shared and equivalently aggregated as a utility-scale storage. While the existing literature has focused on the scheduling of distributed storage, few studies have quantified the accelerated degradation induced by storage sharing or incorporated State of Health (SoH) into storage sharing management. Therefore, we propose a health-aware energy management strategy in the environment of IoS, enabling distributed storage systems to cooperate through information and communication technology. To evaluate the SoH of storage, we design a health-aware framework based on equivalent circuit model (ECM), in which SoH can be derived from the charging behavior of battery. Consequently, the internal resistance, capacity, efficiency, and state of power can be inferred by SoH. To demonstrate the effectiveness of the proposed strategy, three benchmarks, i.e., local health-unaware model, local health-aware model, and Health-unaware Sharing (HuS) model are designed. Case studies based on 600 residential customers in Texas, USA reveal that IoS will cause additional SoH degradation, and the life of energy storage is reduced by 3.01 years. The proposed strategy can extend the energy storage’s service life by 43.13% and has better economic benefits compared with traditional HuS.

Sizeable lithium-ion battery (LIB) sources in the transportation and power sectors provide a promising approach to alleviate the increasing volatility in energy systems. To dispatch LIBs durably and safely, operators need to estimate the battery power characteristics, which are commonly derived from external states of the battery obtained by empirical models. However, the internal states that play a decisive role are rarely considered. In this work, the power characterization is based on an interpretable and analytical electrochemical model. In addition to external states, internal states, including Li-ion concentrations, side reaction rates, and the energy conversion efficiency, are considered in the characterization. Since the dispatch time interval is usually longer than the time resolution of the battery model, an optimization-based approach taking the idea of model predictive control is designed for efficient calculation. A linearization scheme is proposed to embed power characteristics into the optimization-based dispatch of an integrated energy-transportation system with low complexity. Case studies on LiNCM and LiFePO$_{4}$batteries in different temperatures are conducted. The calculation of power characteristics takes about two minutes. By considering power characteristics, the energy conversion efficiency of the dispatched battery can be increased by 5%–15%. At the same time, the degradation stress and heat generation can be reduced to around one-fourth of the naïve case.

As a most promising alternative carrier of energy in the future low-carbon energy system, the possibility of power-to-hydrogen (P2H) generally proton exchange membrane based water electrolyzers, has been explored for frequency response provision. However, there remains an open question as to how to capture the interaction of P2H operation and frequency response provision, and incentivize such behaviors to enhance system resiliency. In this article, we propose a marginal pricing mechanism for frequency response service provision to enhance grid resilience considering the participation of P2H. To depict the interaction between P2H operation and frequency response provision, a dynamic model of P2H is partially simplified to be incorporated into the system frequency response process. Case studies based on the IEEE RTS-24 system and a realistic Northwest power grid of China show that P2H can significantly improve the frequency response ability, especially reduce the startup of conventional units, so as to reduce carbon emissions. This effect is extremely appealing in the renewable-dominated cases.

With the popularity of plug-in electric vehicles (EVs) and the development of the vehicle to grid (V2G) technology, EVs can be aggregated and behave as a controllable storage system via the Internet of Things. However, it remains an open question as to how large-scale EVs can be effectively integrated into the system-level operation. In this article, we propose a dispatchable region formation approach of EV aggregation to capture its available flexibility in microgrid (MG) bidding. The dispatchable region of EV aggregation describes the feasible operation strategy as a single entity, characterized by its power and cumulative energy limits. Instead of scheduling an individual EV, the dispatchable region of large-scale EVs enables the MG operator to directly schedule the EV aggregation toward market revenue maximization. The MG bidding strategy is formulated as a risk-constrained stochastic programming, which maximizes day-ahead market profits considering real-time imbalance settlement in a dual-pricing market. Case studies based on real-world datasets demonstrate that the proposed dispatchable region approach in MG biding can significantly improve both computation efficiency and forecasting accuracy.