Secure and cost-effective operation of low carbon power systems under multiple uncertainties

Power system decarbonisation is driving the rapid deployment of renewable energy sources (RES) like wind and solar at the transmission and distribution level. Their differences from the synchronous thermal plants they are displacing make secure and efficient grid operation challenging. Frequency stability is of particular concern due to the current lack of provision of frequency ancillary services like inertia or response from RES generators. Furthermore, the weather dependency of RES generation coupled with the proliferation of distributed energy resources (DER) like small-scale solar or electric vehicles permeates future low-carbon systems with uncertainty under which legacy scheduling methods are inadequate. Overly cautious approaches to this uncertainty can lead to inefficient and expensive systems, whilst naive methods jeopardise system security. This thesis significantly advances the frequency-constrained scheduling literature by developing frameworks that explicitly account for multiple new uncertainties. This is in addition to RES forecast uncertainty which is the exclusive focus of most previous works. The frameworks take the form of convex constraints that are useful in many market and scheduling problems. The constraints equip system operators with tools to explicitly guarantee their preferred level of system security whilst unlocking substantial value from emerging and abundant DERs. A major contribution is to address the exclusion of DERs from the provision of ancillary services due to their intrinsic uncertainty from aggregation. This is done by incorporating the uncertainty into the system frequency dynamics, from which deterministic convex constraints are derived. In addition to managing uncertainty to facilitate emerging DERs to provide legacy frequency services, a novel frequency containment service is designed. The framework allows a small amount of load shedding to assist with frequency containment during high RES low inertia periods. The expected cost of this service is probabilistic as it is proportional to the probability of a contingency occurring. The framework optimally balances the potentially higher expected costs of an outage against the operational cost benefits of lower ancillary service requirements day-to-day. The developed frameworks are applied extensively to several case studies. These validate their security and demonstrate their significant economic and emission-saving benefits.Open Acces

Risk Hedging Strategies in New Energy Markets

In recent years, two typical developments have been witnessed in the energy market. On the one hand, the penetration of renewable generations has gradually replaced parts of the traditional ways to generate energy. The intermittent nature of renewable generation can lead to energy supply uncertainty, which might exacerbate the imbalance between energy supply and demand. As a result, the problem of energy price risks might occur. On the other hand, with the introduction of distributed energy resources (DERs), new categories of markets besides traditional wholesale and retail markets are emerging. The main benefits of the penetration of DERs are threefold. First, DERs can increase power system reliability. Second, the cost of transmission can be reduced. Third, end users can directly participate in some of these new types of markets according to their energy demand, excess energy, and cost function without third-party intervention. However, energy market participants might encounter various types of uncertainties. Therefore, it is necessary to develop proper risk-hedging strategies for different energy market participants in emerging new markets. Thus, we propose risk-hedging strategies that can be used to guide various market participants to hedge risks and enhance utilities in the new energy market. These participants can be categorized into the supply side and demand side. Regarding the wide range of hedging tools analyzed in this thesis, four main types of hedging strategies are developed, including the application of ESS, financial tools, DR management, and pricing strategy. Several benchmark test systems have been applied to demonstrate the effectiveness of the proposed risk-hedging strategies. Comparative studies of existing risk hedging approaches in the literature, where applicable, have also been conducted. The real applicability of the proposed approach has been verified by simulation results

Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints

A systematic review of machine learning techniques related to local energy communities

In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.publishedVersio

Microgrid Energy Management

In IEEE Standards, a Microgrid is defined as a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries, which acts as a single controllable entity with respect to the grid and can connect and disconnect from the grid to enable it to operate in both grid-connected or island modes. This Special Issue focuses on innovative strategies for the management of the Microgrids and, in response to the call for papers, six high-quality papers were accepted for publication. Consistent with the instructions in the call for papers and with the feedback received from the reviewers, four papers dealt with different types of supervisory energy management systems of Microgrids (i.e., adaptive neuro-fuzzy wavelet-based controls, cost-efficient power-sharing techniques, and two-level hierarchical energy management systems); the proposed energy management systems are of quite general purpose and aim to reduce energy usages and monetary costs. In the last two papers, the authors concentrate their research efforts on the management of specific cases, i.e., Microgrids with electric vehicle charging stations and for all-electric ships