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Electricity Systems in europe are experiencing major changes due to targets for renewable energy integration, reducing greenhouse gas emissions, and energy efficiency. Different studies show that there is a growing need for more flexibility and active stakeholder involvement at all levels (from small consumers to pan-European networks) to ensure the efficient and reliable operation of the electricity system, particularly to deal with growing volumes of renewable energy sources, from transmission-level wind and solar farms to household-level photovoltaic generation. Other key evolutions that aim to decarbonize the energy sector beyond electricity, such as those based on the electrification of energy end uses (e.g., the development of electric vehicles and the electrification of heating), are also expected to have a substantial impact. [ABSTRACT FROM AUTHOR]

• Price arbitrage operation of distributed battery storage is examined. • Local energy system and national power system interactions are considered. • Reformulated bilevel optimisation as a single level mixed integer linear problem. • Comparison of centralised and bilevel optimisations of distributed battery storage. • Influence of retail contracts on value of distributed battery storage investigated. Battery storage provides flexibility to the power system and supports the increased integration of renewable energy sources. Distributed battery storage systems that are behind the meter are operated by their local owners, whose objectives may not align with those of the national power system. This paper presents a Bilevel optimisation approach to investigate the exchange of electricity between distributed battery storage and the national power system. The independent operating objectives of the battery storage systems are explicitly considered to assess their impact on the operation of the national power system. A comparison with a Centralised optimisation approach, that assumes a single objective function for the whole system, shows that the Bilevel optimisation approach captures the independencies of distributed battery storage objectives, while accounting for its interactions with the wider power system. The results show that the Centralised optimisation approach tends to overestimate the value of distributed battery storage for the power system. The results also highlight the influence of the retail contract structure in maximising the value of distributed battery storage for the national power system. [ABSTRACT FROM AUTHOR]

With the introduction of new technologies, such as waste heat recovery units (WHRU), associated gas utilization, the energy flow coupling relationship is further deepened within the energy system of the offshore oil and gas production platform. Besides, the energy system is closely linked with the oil and gas production system, and a closed-loop relationship between energy flow and material flow can be revealed. Uncertainties of energy supply and production process may lead to system-wide fluctuations, which threaten the stable operation of the platform. Therefore, an optimal planning model of integrated energy system for offshore oil and gas production platform is proposed in this paper. Firstly, a generalized energy and material flow model is proposed, three matrixes are defined based on laws of thermodynamics, including energy matrix, process matrix and feedback matrix. Secondly, the energy-material conversion relationship between the energy system and production system of a typical offshore oil and gas platform is quantitatively described, together with the coupling between the input and output of the two systems. Thirdly, considering the energy-material balance constraints and the uncertainties of production system, a multi-objective stochastic planning model for the offshore integrated energy system is established, which takes economics and environmental protection into consideration. A Monte Carlo simulation-based NSGA-II algorithm is proposed to solve the model. Finally, the validity and feasibility of the proposed methodology are demonstrated through an offshore oil and gas platform in Bohai, China. Compared with the traditional planning method, the total cost and CO2 emissions of the proposed method are reduced by 18.9% and 17.3%, respectively. [ABSTRACT FROM AUTHOR]

The integration of large capacities of wind and solar generation into the France and Great Britain (GB) power systems is expected to pose significant operational challenges. Electricity interconnectors can play a role in facilitating the integration of renewable generation in neighbouring countries by allowing power to flow freely between power systems and therefore smooth the net electricity demand. In this paper, role of the electricity interconnectors in efficient balancing of supply and demand in the France and GB power systems was evaluated in terms of overall reduction in the operational costs and curtailment of renewable generation, and also its impact on operation of gas-fired plants. The value of the France-GB interconnectors was studied for two generation mix scenarios in 2030 using PLEXOS. The outputs of the modelling showed the interconnectors will result in larger amount of wind and solar to be absorbed by both power systems which consequently will reduce overall operational costs and CO 2 emissions. In addition, the interconnectors will reduce burden on gas-fired plants compensating for variation in wind and solar generation. This can have a significant value in operation and required investment of gas networks in both countries. [ABSTRACT FROM AUTHOR]

Governments' net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advanced information and control infrastructures of smart grids which allow the interoperability among various stakeholders. Under this circumstance, increasing number of consumers produce, store, and consume energy, giving them a new role of prosumers. The integration of prosumers and accommodation of incurred bidirectional flows of energy and information rely on two key factors: flexible structures of energy markets and intelligent operations of power systems. The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems. This paper attempts to address how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets. To achieve this objective, first, this paper reviews how policy designs price carbon emissions caused by the fossil-fuel based generation so as to facilitate the integration of prosumers with renewable energy sources. Second, the potential structures of energy markets with the support of the blockchain technologies are discussed. Last, how to apply the AI for enhancing the state monitoring and decision making during the operations of power systems is introduced. • The use of blockchain and artificial intelligence for facilitating prosumers to be integrated into smart grids and decarbonising the power systems. • Review how policy designs price of carbon emissions to facilitate the integration of prosumers with renewable energy sources. • Future energy markets with blockchain technologies. • How to apply artificial intelligence technologies for enhancing the control and decision-making in smart grids. [ABSTRACT FROM AUTHOR]

• Emissions may not be reduced under current government guidelines. • Efforts must be made to increase renewables building rates. • Fossil fuels will continue to be important to ensure demand attendance. The use of renewable energy sources in electricity systems has become an important strategy to reduce greenhouse gas emissions in the power sector and to avoid depletion of natural resources. However, uncertainties on renewable power generation, diurnal and seasonal variability, technology costs and regional resources availability are factors that have challenged their large-scale adoption. In this context, studies have been focused on how to best integrate different generation technologies with regards to costs and security of supply. Taking the Brazilian electricity system expansion as a case study, this paper proposes a new hierarchical modelling framework that integrates two electricity planning optimization models: (i) a portfolio theory-based model that deals with the trade-off between security of supply and expected supply cost and (ii) a deterministic least cost model that finds the amount of electricity to be produced by each generation technology. The proposed approach can provide information regarding expected total supply cost, electricity deficit risks, capacity expansion and expected greenhouse gas emissions. Additionally, current government guidelines for renewables expansion are discussed with respect to the Brazilian targets for emissions reduction. Results point to a slight increase in renewables share from 82.4% in 2020 to 85.3% in 2035; however maximum established building rates for renewable generation projects are shown to be very tight and may hamper the achievement of emissions reduction. Overall, it is shown that renewable technologies are attractive for both the environmental and economic spheres, but fossil fuels will continue to play an important role for meeting demand. [ABSTRACT FROM AUTHOR]

The emerging role of energy prosumers (both producers and consumers) enables a more flexible and localised structure of energy markets. However, it leads to challenges for the energy scheduling of individual prosumers in terms of identifying idiosyncratic pricing patterns, cost-effectively predicting power profiles, and scheduling various scales of generation and consumption sources. To overcome these three challenges, this study proposes a novel data-driven energy scheduling model for an individual prosumer. The pricing patterns of a prosumer are represented by three types of dynamic price elasticities, i.e., the price elasticities of the generation, consumption, and carbon emissions. To improve the computational efficiency and scalability, the heuristic algorithms used to solve the optimisation problems is replaced by the convolutional neural networks which map the pricing patterns to scheduling decisions of a prosumer. The variations of uncertainties caused by the intermittency of renewable energy sources, flexible demand, and dynamic prices are predicted by the developed real-time scenarios selection approach, in which each variation is defined as a scenario. Case studies under various IEEE test distribution systems and uncertain scenarios demonstrate the effectiveness of our proposed energy scheduling model in terms of predicting scheduling decisions in microseconds with high accuracy. [Display omitted] • A novel data-driven prosumer-centric energy scheduling model to improve the computational efficiency and scalability. • Convolutional neural networks map pricing patterns to potential scheduling decisions. • Real-time scenarios selection to adapt uncertain scenarios with dynamic operations of prosumers. • Case studies validate the efficiency under various IEEE test distribution systems and uncertain scenarios. [ABSTRACT FROM AUTHOR]

• Proposing optimization model for gas and power systems. • Incorporating resiliency analysis algorithm into the integrated operation of gas and power systems. • Proposing a novel possibilistic approach to deal with uncertainty. • Investigating the role of demand response and electrical vehicles. Gas-fired power plants are commonly employed to deal with the intermittency of renewable energy resources due to their flexible characteristics. Therefore, the intermittency in the power system transmits to the gas system through the gas-fired power plants, which makes the operation of these systems even more interdependent. This study proposes a novel possibilistic model for the integrated operation of gas and power systems in the presence of electric vehicles and demand response. The model takes into account uncertainty in demand prediction and output power of wind farms, which is based on possibility and necessity theories in fuzzy logic through modeling uncertain parameters by Gaussian membership function. Moreover, a contingency analysis algorithm based on maximin optimization is developed to enhance the resiliency in the integrated operation of these systems by finding the worst-case scenario for the outage of components. The proposed model is implemented on a Belgium gas network and IEEE 24-bus electricity network. It is demonstrated that the possibilistic model allows the gas network to respond to the demand variations by providing a sufficient level of linepack within the pipelines. As a result, gas-fired power plants are supposed to commit more efficiently to cope with the intermittency of wind farms, which reduce the wind curtailment by 26%. Furthermore, it is quantified that through applying the contingency analysis algorithm in presence of demand response and electrical vehicles, the costs of operation and load shedding is reduced up to 17% and 83%, respectively. [ABSTRACT FROM AUTHOR]