Moving to a user-centric approach is seen as a key change of paradigm in order to increase the efficiency and sustainability of energy systems. Massive integration of new economic agents such as prosumers and mobile or stationary storage will play a key role in the energy transition. In this paper, a design of a community-based local energy market (CB-LEM) is proposed where the members are allowed to trade energy among each other through a local pool. The price is set on a day-ahead basis under the coordination of a Community Manager (CM). The novel aspect of this work is that every agent takes part in the determination of the local market price while deciding its own scheduling problem under uncertainty concerning renewable-energy generation and storage. After day-ahead clearing, real time operation and ex-post settlement of the local market by the CM are also explained in order to complete the proposed design. A real case study in a neighbourhood in Amsterdam, The Netherlands, is used for testing the proposed framework. In addition, the performance of the ADMM-based clearing process is analyzed in terms of scalability and convergence.

The increased deployment of distributed generation calls for the coordination and interaction between the transmission and distribution levels. This requirement is particularly relevant for planning purposes when renewable-based generation is involved. Unfortunately, in current industry practice, transmission and distribution network planners solve their problems independent of each other, thereby leading to suboptimal solutions. Within this context, this paper addresses the integrated expansion planning problem of transmission and distribution systems where investments in network and generation assets are jointly considered. Several alternatives are available for the installation of lines as well as conventional and renewable-based generators at both system levels. Thus, the optimal expansion plan identifies the best alternative for the candidate assets under the uncertainty associated with demand and renewable-based power production. The proposed model is an instance of stochastic programming wherein uncertainty is characterized through a set of scenarios that explicitly capture the correlation between the uncertain parameters. The resulting stochastic program is driven by the minimization of the expected total cost, which comprises the costs related to investment decisions and system operation. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program for which finite convergence to optimality is guaranteed. Numerical results show the effective performance of the proposed approach.

Uncertainty characterization is an essential component of decision-making problems in electricity markets. In this work, a class-driven approach is proposed to describe stochasticity. The methodology consists of a three-step system that includes a class allocation component, a generative element based on a long short-term memory neural network and an automated reduction method with a variance-based continuation criterion. The system is employed and evaluated on Dutch imbalance market prices. Test results are presented, expressing the proficiency of the approach, both in generating realistic scenario sets that reflect the erratic dynamics in the data and adequately reducing generated sets without the need to explicitly and manually predetermine the cardinality of the reduced set.

Nowadays, power systems’ Protection, Automation, and Control (PAC) functionalities are often deployed in different constrained devices (Intelligent Electronic Devices) following a coupled hardware/software design. However, with the increase in distributed energy resources, more customized controllers will be required. These devices have high operational and deployment costs with long development, testing, and complex upgrade cycles. Addressing these challenges requires that a ’revolution’ in power system PAC design takes place. Decoupling from hardware-dependent implementations by virtualizing the functionalities facilitates the transition from a traditional power grid into a software-defined smart grid. This article presents a survey of recent literature on software-defined PAC for power systems, covering the concepts, main academic works, industrial proof of concepts, and the latest standardization efforts in this rising area. Finally, we summarize the expected future technical, industrial, and standardization challenges and open research problems. It was observed that software-defined PAC systems have a promising potential that can be leveraged for future PAC and smart grid developments. Moreover, standardizations in virtual IED software development and deployments, configuration tools, performance benchmarking, and compliance testing using a dynamic, agile approach assuring interoperability are critical enablers.

Offshore wind farms are growing in complexity and size, expanding deeper into maritime environments to capture stronger and steadier wind energy. Like other domains in the energy sector, the wind energy domain is continuing to digitalize its systems by embracing Industry 4.0 technologies such as the Industrial Internet of Things (IIoT), virtualization, and edge computing to monitor and manage its critical infrastructure remotely. Adopting these technologies creates dynamic, scalable, and cost-effective data-acquisition systems. At the heart of these data-acquisition systems is a communication network that facilitates data transfer between communicating nodes. Given the challenges of configuring, managing, and troubleshooting large-scale communication networks, this review paper explores the adoption of the state-of-the-art software-defined networking (SDN) and network function virtualization (NFV) technologies in the design of next-generation offshore wind farm IIoT–Edge communication networks. While SDN and NFV technologies present a promising solution to address the challenges of these large-scale communication networks, this paper discusses the SDN/NFV-related performance, security, reliability, and scalability concerns, highlighting current mitigation strategies. Building on these mitigation strategies, the concept of resilience (that is, the ability to recover from component failures, attacks, and service interruptions) is given special attention. The paper highlights the self-X (self-configuring, self-healing, and self-optimizing) approaches that build resilience in the software-defined IIoT–Edge communication network architectures. These resilience approaches enable the network to autonomously adjust its configuration, self-repair during stochastic failures, and optimize performance in response to changing conditions. The paper concludes that resilient software-defined IIoT–Edge communication networks will play a big role in guaranteeing seamless next-generation offshore wind farm operations by facilitating critical, latency-sensitive data transfers.