As an important market entity, energy storage (ES) can participate in multiple electricity markets simultaneously and benefit from them. However, the current operation models for ES primarily concentrate on calculating market revenue, which fails to assess the risk and correlation of market prices accurately. Thus, this article designs an operation method for ES considering price risk and coupling in markets. First, a multiplexing scheme is designed for ES operation in multimarket, considering market price risk and market coupling. Second, generalized autoregressive conditional heteroskedasticity and exponentially weighted moving average models are introduced to precisely assess price risk, and a dynamic Copula method is designed to reflect the coupling coefficient. Furthermore, it introduces the concept of expected premium calculated through real options theory, allowing for more refined profit maximization strategies under positive fluctuating market conditions. Demonstrated with a real PJM multimarket-based framework, the proposed method leads to an 18.2% increase compared to the traditional portfolio method, which provides a new business model for ES to unlock its potential value.

Decentralized trading schemes involving energy prosumers have prevailed in recent years. Such schemes provide a pathway for increased energy efficiency and can be enhanced by the use of blockchain technology to address security concerns in decentralized trading. To improve transaction security and privacy protection while ensuring desirable social governance, this article proposes a novel two-stage blockchain-based operation and trading mechanism to enhance energy hubs connected with integrated energy systems (IESs). This mechanism includes multienergy aggregators (MAGs) that use a consortium blockchain and its enabled proof-of-work (PoW) to transfer and audit transaction records, with social governance principles for guiding prosumers’ decision-making in the peer-to-peer (P2P) transaction management process. The uncertain nature of renewable generation and load demand are adequately modeled in the two-stage Wasserstein-based distributionally robust optimization (DRO). The practicality of the proposed mechanism is illustrated by several case studies that jointly show its ability to handle an increased renewable generation capacity, achieve a 16.7% saving in the audit cost, and facilitate 2.4% more P2P interactions. Overall, the proposed two-stage blockchain-based trading mechanism provides a practical trading scheme and can reduce redundant trading amounts by 6.5%, leading to a further reduction of the overall operation cost. Compared to the state-of-the-art benchmark methods, our mechanism exhibits significant operation cost reduction and ensures social governance and transaction security for IES and energy hubs.

The renewable energy community (REC) is a prosperous scheme to promote distributed renewable resources in the city and suburban areas. Although energy storage (ES) is essential to smooth the volatility of REs, it is costly for small-scale REC investing ES to increase its profits in the frequency regulation market (FRM). Thus, renting ES capacity is an alternative way to boost the development of REC. With rented ES capacity, there are three barriers: 1) the correspondence between the ES capacity rental mode and the frequency regulation mileage performance; 2) the cooperated operation method between REC and ES; 3) the uncertainties of renewables in the FRM. This article proposes an operation strategy for REC with rented ES capacity in FRM under renewable and price uncertainties. First, considering regulation capacity and performance prices, an FRM is modeled with regulation up and down services. Second, the ES capacity rental model is designed with normal rental mode and extra rental mode based on the low and high ratios between the mileage and capacity. Then, to unify the output of REC with capacity rental in FRM, a cooperated operation method is proposed considering the self-scheduled curtailment of renewables and the interaction between the renewables and ES. The total profits of REC are maximized with the uncertainties of REC output, loads and prices via the weighted sum of the value in multi scenarios. The proposed method is demonstrated with an REC and the results show that the total profits are enhanced by nearly 2 times with the proposed method.

Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model’s effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water–energy–social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

Resilience problems from cyber-attacks on information communication technologies exist under their wide usage. False data injection (FDI) judiciously designed by attackers may cause severe consequences such as uneconomic operation and blackouts, particularly multivector energy distribution systems (MEDS), which are closely linked and interdependent. This article addresses the cyber resilient issues of an MEDS caused by FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein metric-based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, illustrating the effectiveness of the proposed cybersecured model. The produced adaptive robust economic operation for MEDS can reduce load shedding and enhance system resilience against severe cyberattacks.