The increasing penetration of storage-like resources calls for the utilization of temporally coupled flexibility to guarantee the secure and economic operation of power systems. Due to the large number and heterogeneous characteristics of such resources, hierarchical dispatch methods based on the exchange of feasible operation regions (FORs) have attracted widespread attention. In such a framework, virtual power plants (VPPs) that aggregate different resources need to provide the aggregated FOR and schedule the resources according to the system dispatch order. However, aggregating the exact multi-interval FOR of VPPs becomes computationally intractable when the number of coordination variables increases. To this end, a coordinated dispatch framework based on dynamic FOR aggregation is proposed to allow the modification of the relaxed FOR according to the current pre-dispatch order. A dynamic FOR formulation method is then proposed to derive the active cost and feasibility constraints based on the dual of the VPP’s operation problem. The proposed method is guaranteed to converge in a finite number of iterations and reach a feasible dispatch solution that is consistent with the global optimum. Its application in the coordinated dispatch between a distribution system operator and VPPs is then studied. Numerical studies on the IEEE 33-bus and Caracas 141-bus distribution systems validate the effectiveness of the proposed method. For the IEEE 33-bus system with 24 time intervals, the proposed method reaches the global optimum within 81 iterations, eliminates an initial relative tracking error of 8.71%, and reduces the DSO’s operation cost by 4.68% compared with the zonotope-based approximate FOR. For the 141-bus system, the relative tracking errors decrease to at most 0.40% after 50 iterations, demonstrating the scalability to larger systems.

Energy sharing has gained substantial popularity in improving the controllability of distributed energy resources (DERs). In this chapter, we introduce the concept of sharing economy as a novel and promising business model in microgrids. We first compare the operation schemes with and without energy sharing, which explains the role of sharing economy in energy sectors. The models of energy sharing toward the applications in energy and energy/capacity markets are formulated. To guarantee that every DER has incentive to participate in energy sharing, we design an incentive mechanism that achieves a fair benefit allocation according to diversified contributions, which is theoretically an asymmetric Nash bargaining framework. Then, the centralized model is decomposed via alternating direction method of multipliers. Case studies validate the benefits of energy sharing in peer-to-peer trading in microgrids.

A virtual battery (VB) provides a succinct interface for aggregating distributed storage-like resources (SLR) to interact with a utility-level system. To overcome the drawbacks of existing VB models, including conservatism and neglecting network constraints, this paper optimizes the power and energy parameters of VB to enlarge its flexibility region. An optimal VB is identified by a robust optimization problem with decision-dependent uncertainty. An algorithm based on the Benders decomposition is developed to solve this problem. The proposed method yields the largest VB satisfying constraints of both network and SLRs. Case studies verify the superiority of the optimal VB in terms of security guarantee and less conservatism.

The superior performance of deep learning relies heavily on a large collection of sample data, but the data insufficiency problem turns out to be relatively common in global electricity markets. How to prevent overfitting in this case becomes a fundamental challenge when training deep learning models in different market applications. With this in mind, we propose a general framework, namely Knowledge-Augmented Training (KAT), to improve the sample efficiency, and the main idea is to incorporate domain knowledge into the training procedures of deep learning models. Specifically, we propose a novel data augmentation technique to generate some synthetic data, which are later processed by an improved training strategy. This KAT methodology follows and realizes the idea of combining analytical and deep learning models together. Modern learning theories demonstrate the effectiveness of our method in terms of effective prediction error feedbacks, a reliable loss function, and rich gradient noises. At last, we study two popular applications in detail: user modeling and probabilistic price forecasting. The proposed method outperforms other competitors in all numerical tests, and the underlying reasons are explained by further statistical and visualization results.

Market participants can only bid with lagged information disclosure under the existing market mechanism, which can lead to information asymmetry and irrational market behavior, thus influencing market efficiency. To promote rational bidding behavior of market participants and improve market efficiency, a novel electricity market mechanism based on cloudedge collaboration is proposed in this paper. Critical market information, called residual demand curve, is published to market participants in real-time on the cloud side, while participants on the edge side are allowed to adjust their bids according to the information disclosure prior to closure gate. The proposed mechanism can encourage rational bids in an incentive-compatible way through the process of dynamic equilibrium while protecting participants' privacy. This paper further formulates the mathematical model of market equilibrium to simulate the process of each market participant's strategic bidding behavior towards equilibrium. A case study based on the IEEE 30-bus system shows the proposed market mechanism can effectively guide bidding behavior of market participants, while condensing exchanged information and protecting privacy of participants.

Coordinated optimal dispatch is of utmost importance for the efficient and secure operation of hierarchically structured power systems. Conventional coordinated optimization methods, such as the Lagrangian relaxation and Benders decomposition, require iterative information exchange among subsystems. Iterative coordination methods have drawbacks including slow convergence, risk of oscillation and divergence, and incapability of multi-level optimization problems. To this end, this paper aims at the non-iterative coordinated optimization method for hierarchical power systems. The theory of the equivalent projection (EP) is proposed, which makes external equivalence of the optimal dispatch model of the subsystem. Based on the EP theory, a coordinated optimization framework is developed, where each subsystem submits the EP model as a substitute for its original model to participate in the cross-system coordination. The proposed coordination framework is proven to guarantee the same optimality as the joint optimization, with additional benefits of avoiding iterative information exchange, protecting privacy, compatibility with practical dispatch scheme, and capability of multi-level problems.

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.

In the current design exploration of the future, there are two distinct categories, one is the pursuit of what will be possible, and the other is the exploration of what is possible. In order to better distinguish the characteristics and functions of them, this study tries to divide future-oriented design into affirmative design and alternative design from the different future cones explored by them. Affirmative design takes current solutions as the premise and pursues faster, better, smaller and cheaper solutions. Ostensibly, affirmative design is a pursuit of perfection, but in essence, it is a kind of patching method which may suppress designers’ innovation for the future and further restrict people’s cognition of the futures. Alternative design is a design method of shelving the reality, trying to seek other possibilities outside the status quo. Its essence is a method of seeking “others”. The difficulty lies in that designers need to get rid of the current comfort and complacency, and their works may become castles in the air. The point of this study is that we need some sort of balance: not to be trapped in a single future, and not to be trapped in an “escape from a single future”. On the basis of looking for more possible future, find a path to realize it, so that alternative design will be chosen by people and then transformed into affirmative design.

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. With this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, policy making, or educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Several support methods and metrics are then implemented in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Based on these, we conduct three empirical studies on the U.S. power systems and markets to introduce some new solutions and unexpected findings. This conveys a more complete picture of the pandemic's impacts, and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.

新型电力系统迫切需要发展各种灵活性调节资源,必须要为灵活性调节资源设计合理的市场机制.但储能、调节式水电站等电量约束型机组的成本特性与传统电源有显著差别,基于火电为主体的电力系统设计的现货市场机制不适应电量约束型机组的特点.为此,分析了电量约束型机组的特性差异,发现其稀缺资源由电力转为电量,其成本与电量而非出力水平相关,进而论证了针对电量约束型机组特点、构建全新报价机制的必要性.在此基础上,提出了电量约束型机组在现货市场的报价机制,由申报功率量价曲线变为申报电量量价曲线,设计了考虑电量约束型机组的新型电力系统现货市场出清模型.为了激励储能参与现货市场,还提出了与现货市场相耦合的储能容量机制.算例验证了所提机制和模型的有效性.

High-frequency activity (HFA) in intracranial electroencephalography recordings are diagnostic biomarkers for refractory epilepsy. Clinical utilities based on HFA have been extensively examined. HFA often exhibits different spatial patterns corresponding to specific states of neural activation, which will potentially improve epileptic tissue localization. However, research on quantitative measurement and separation of such patterns is still lacking. In this paper, spatial pattern clustering of HFA (SPC-HFA) is developed. The process is composed of three steps: (1) feature extraction: skewness which quantifies the intensity of HFA is extracted; (2) clustering: k-means clustering is applied to separate column vectors within the feature matrix into intrinsic spatial patterns; (3) localization: the determination of epileptic tissue is performed based on the cluster centroid with HFA expanding to the largest spatial extent. Experiments were conducted on a public iEEG dataset with 20 patients. Compared with existing localization methods, SPC-HFA demonstrates improvement (Cohen’s d $>0.2$ ) and ranks top in 10 out of 20 patients in terms of the area under the curve. In addition, after extending SPC-HFA to high-frequency oscillation detection algorithms, corresponding localization results also improve with effect size Cohen’s d $\geq 0.48$ . Therefore, SPC-HFA can be utilized to guide clinical and surgical treatment of refractory epilepsy.

As data centers emerge as the information backbone of an increasingly digital world, the associated service demand has been rising rapidly. The operational nature of the data center brings great flexibility potential for energy systems. However, there remains an open question as to how to capture the flexibility potentials of geographically dispersed data centers and incentivize their flexibility provision for system operation. In this paper, we propose electricity–heat coordinated operation to capture the flexibility potential of data centers, i.e., temporal, spatial and integrated energy flexibility. To better depict the interaction between the system operator and the data centers, the centralized coordinated operation model is decomposed into a bilevel model, and an incentivized profit-sharing mechanism is designed to suitably motivate the flexibility provision of data centers. Case studies based on real-world datasets show that incorporating data centers at the system level can effectively improve operation efficiency and facilitate renewable energy integration. Hopefully, our work can provide novel insights into the coordination of data networks and power grids as well as the flexibility potential activation of data centers.

This letter proposes a novel framework for modeling the response-price relationship of intertemporally responsive loads (IRL) using historical data. This task is cast as a data-driven inverse optimization (DDIO) problem, which trains a surrogate model whose best response to electricity price most closely resembles the observed power trajectory of IRLs. The virtual battery fleet with an adjustable number of elements is used as the surrogate model, which yields a linear modeling result. The DDIO is a bilevel programming problem. To solve it efficiently, a Newton-based algorithm with a grid fitting initialization technique is developed. The accuracy and robustness of the proposed modeling method are validated by numerical tests in comparison with other machine learning regressors.

Network-constrained unit commitment (NCUC) is one of the most widely used applications in power system and electricity market operations. According to empirical evidence, some of the transmission constraints in a NCUC are inactive. Identifying and eliminating these inactive constraints can improve the efficiency. In this paper, an efficient method is first proposed for identifying the inactive transmission constraints. The physical and economic insights of NCUC are carefully considered and utilized. Both the generating costs and power transfer distribution factor (PTDF) are considered. Not only redundant constraints but also non-binding constraints can be identified via the proposed method. An acceleration method that combines relaxation-based neighborhood search and improved relaxation inducement is proposed for further reducing the computation time. The case study shows that the proposed method can significantly reduce the number of transmission constraints and substantially improve the efficiency of NCUC without impacting the optimality.

The coordination between local and wholesale markets has been widely regarded as an effective means to improve the utilization of demand side resources. Existing literature has begun to explore the feasible region for incorporating local electricity markets into the wholesale level via projection algorithms. However, it remains challenging to deal with the complex constraints of inner spatiotemporal coupling. To achieve efficient interaction between local and wholesale markets, this paper proposes a local electricity market representation method based on umbrella constraints extraction. In contrast to the existing projection methods, we theoretically derive the closed form of a projection-based feasible region, which can be characterized as a constraint set formed by the extreme points of the dual space of the proposed diagnostic methods. A local market is equivalently formulated as a condensed model represented by its temporal-coupled feasible region (TCFR). To reduce the redundant constraints of TCFR, an umbrella constraint identification method is developed for the minimal representation of the problem. Case studies based on a modified IEEE 33-bus feeder and a realistic distribution grid in Venezuela demonstrate the effectiveness and computational efficiency of our proposed method.

With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of “access over ownership”, the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

Nowadays, with products and services getting more intelligent and evolving faster, the human-centred design methods, such as Design Thinking, have presented some areas that they can't cover. Design Thinking takes the rapid feedbacks of users as the substitute for subjective speculation and long-term consideration of designers, which makes the design suitable for solving current problems but relatively short-sighted. In this context, some researchers have begun to explore ways to map out a more permanent, constant values behind products and services, to plan a longer vision of design. In many explorations, we found the concept and method of Futures Thinking to inspire Design for farsighted. With it we can explore and enlarge the future possibilities of design, make design adapt to various futures. On this basis, we carried out researches on what kind of design can reflect the characteristics of Futures Thinking. And, this paper focuses on pointing out the features of this type of design. Through interviews and text deconstruction and reconstruction, this paper summarizes the keywords of the design features on Futures Thinking. It is hoped that the outline and overview of such type of design can contribute to the discussion of design on Futures thinking, so that more people can participate in the dialogue and discussion about the futures, to design the preferable futures.

能源互联网的建设不仅需要技术手段的支撑,更需要以价值为驱动激活各方主体参与,打造互惠共赢的能源生态圈.为此,从能源、信息和制度经济学的视角出发,深入分析能源互联网的价值创造机理、业态创新范式和发展战略选择.首先,剖析了能源互联网的价值创造机理,其本质是以多能融合突破孤立系统的边界,从而显著扩大资源优化配置的空间;通过信息技术使得能源系统更加有序,进而促进能源系统的"熵减";通过能源互联网平台释放范围经济的巨大红利.其次,设计了基于共享经济机制、降低交易成本、激励共享共赢的能源互联网价值分配机制,谋划了能源互联网支撑智慧城市发展、支撑绿电追踪、支撑能源消费节能降耗、提升工业资产利用率、助力政府治理的新业态.最后,策划了以再电气化为核心目标、基于价值公平分配的数据共享、以数字孪生为平台的人工智能、以负荷调度替代需求侧响应、基于共享经济的能源互联网市场机制的发展战略.

现货市场的交易品种包括能量商品及备用等辅助服务商品.当前备用辅助服务尚未形成供需双方均可申报量、价的市场,现有市场难以激发备用需求侧的互动弹性,尚需研究符合"谁产生,谁承担"原则的备用费用分摊机制.文中提出了基于备用辅助服务需求申报的现货市场组织方法,探索了通过市场主体备用需求申报的市场组织模式激发备用需求弹性的可行性;提出了考虑备用辅助服务需求申报及备用需求弹性的市场出清模型,所提模型保持了线性建模形式,求解难度符合市场出清需求.同时,根据备用辅助服务的供需特性提出了结算方法,可体现激励相容的市场组织原则.所提方法的有效性在不同新能源接入比例情景下得到了验证.