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Summary: This paper investigates the potential role of the electricity interconnectors in improving the security of supply in Great Britain (GB) in 2030. Real electricity demand and price data for GB and France in 2016 were used to understand the relationship between power exchange between the two countries and their wholesale electricity prices. A linear programming optimisation model was developed to find the economic power dispatch. Two interconnection links were considered; two‐way trade interconnector with a capacity of 5.4 GW and a 12.3 GW import‐only interconnector between GB and other states. The GB–France link transmits electricity from cheaper system to the more expensive one. The total electricity demand in 2030 will be 406 TWh. Gas‐fired power plants w/wo CCS will provide 83 TWh of the total electricity demand, whereas nuclear power plants will produce 74 TWh. In addition, wind farms and solar PVs are expected to deliver ~120 TWh electricity. CHP units will provide 88 TWh electricity in 2030. The electricity traded between GB and France in 2030 was found to be 33 TWh, which is 160% larger compared with 2016. The power import from France is about 27 TWh and occurs in 59% of the time. For 64% of the time, the interconnector with France is fully loaded. The electricity imported via the 12.3 GW interconnector in 2030 is 1 TWh and mainly occurs during winter‐time when the demand in GB is high. De‐rated capacity margin was calculated based on instantaneous electricity demand and varies between −2% and 139%. The impact of the price of the imported electricity via the 12.3 GW link was investigated. Increasing the price of the imported electricity via the 12.3 GW link results in a higher capacity factor for all the generation options except the 12.3 GW interconnector link. [ABSTRACT FROM AUTHOR]

Power-to-gas (PtG) converts electricity into hydrogen using the electrolysis process and uses the gas grid for the storage and transport of hydrogen. Hydrogen is injected into a gas network in a quantity and quality compatible with the gas safety regulations and thereby transported as a mixture of hydrogen and natural gas to demand centres. Once integrated into the electricity network, PtG systems can provide flexibility to the power system and absorb excess electricity from renewables to produce hydrogen. Injection of hydrogen into the gas network reduces gas volumes supplied from terminals. In order to investigate this concept, hydrogen electrolysers were included as a technology option within an operational optimisation model of the Great Britain (GB) combined gas and electricity network (CGEN). The model was used to determine the minimum cost of meeting the electricity and gas demand in a typical low and high electricity demand day in GB, in the presence of a significant capacity of wind generation. The value of employing power-to-gas systems in the gas and electricity supply system was investigated given different allowable levels of hydrogen injections. The results showed that producing hydrogen from electricity is capable of reducing wind curtailment in a high wind case and decreasing the overall cost of operating the GB gas and electricity network. The northern part of GB was identified as a suitable region to develop hydrogen electrolysis and injection facilities due to its vicinity to a significant capacity of wind generation, as well as the existence of gas network headroom capacity, which is expected to increase as a result of depletion of UK domestic gas resources. [ABSTRACT FROM AUTHOR]

In many power systems, in particular in Great Britain (GB), significant wind generation is anticipated and gas-fired generation will continue to play an important role. Gas-fired generating units act as a link between the gas and electricity networks. The variability of wind power is, therefore, transferred to the gas network by influencing the gas demand for electricity generation. Operation of a GB integrated gas and electricity network considering the uncertainty in wind power forecast was investigated using three operational planning methods: deterministic, two-stage stochastic programming, and multistage stochastic programming. These methods were benchmarked against a perfect foresight model which has no uncertainty associated with the wind power forecast. In all the methods, thermal generators were controlled through an integrated unit commitment and economic dispatch algorithm that used mixed integer programming. The nonlinear characteristics of the gas network, including the gas flow along pipes and the operation of compressors, were taken into account and the resultant nonlinear problem was solved using successive linear programming. The operational strategies determined by the stochastic programming methods showed reductions of the operational costs compared to the solution of the deterministic method by almost 1%. Also, using the stochastic programming methods to schedule the thermal units was shown to make a better use of pumped storage plants to mitigate the variability and uncertainty of the net demand. [ABSTRACT FROM PUBLISHER]

Abstract: A large amount of wind power generation capacity is expected to be installed in Great Britain by 2020. A considerable amount of this capacity will be located in Scotland. This will require reinforcement of power transmission capacity between Scotland and England. Analysis of the impact of a large amount of wind generation capacity on the electricity network requires modelling of wind power output. Due to the lack of available wind power output data a method for artificially generating wind power output from real wind speed data was developed. The technique is based on monthly wind speed averages used to generate Weibull distributions. Hourly wind power output time series over one year are generated for 18 onshore and offshore locations in Great Britain. Capacity factors, maxima, minima and hourly output variations, their means and standard deviations are calculated. A comparison of statistical parameters of the generated data with those of real wind power output shows that both have similar statistical properties. The geographically distributed wind power output data is used in conjunction with an optimal load flow model for analysing three network case studies. One case study uses a hypothesised 2020 Great Britain transmission network the other two cases enhance the network with an Eastern and Western HVDC line linking Scotland and England. The simulation results illustrate the effectiveness of both HVDC lines in reducing wind generation curtailments and operational costs. [Copyright &y& Elsevier]

The power sector is undergoing a period of significant change, in terms of the mix of generation technologies, as well as the structure of energy markets, regulation and assets ownership. The shift to a more liberalised electricity system has resulted in an increase in the number of decision makers. This paper demonstrates an agent-based approach for investigating the long-term investment in the GB power generation sector, whilst considering the operability of the system. A key focus of this study is to investigate the efficacy of a range of policies to reduce the emissions and facilitate investment in renewable generation and battery storage. In order to capture the value of battery storage, the hourly operation of the electricity system, considering short-term variation of demand, renewable generation and wholesale electricity prices (including negative prices during high renewable and low demand events), was incorporated in the long-term investment model. The modelling results show while the cost of battery storage is expected to decrease gradually in future, a substantial subsidy is still required to justify investment in battery storage. The deployment of battery storage provides a significant reduction in the overall power generation system cost, peak demand and carbon emissions. [ABSTRACT FROM AUTHOR]

The electrification of heat supply is a widely discussed strategy for decarbonizing the heat sector in the Great Britain (GB). This impacts the electricity load duration curve and affects the optimal mix of power generation technologies. Additionally, the price of electricity and the emission from the grid determine whether the electrified heat is cost-effective and low carbon. These interdependencies necessitate adopting an integrated approach for long term planning of heat and electricity supplies to ensure cost-effective decarbonization. In this work, we have developed an iterative approach for investigating optimal mix of technologies in electricity and heat sectors considering the interactions between these sectors. This approach was applied to GB as a case study. Firstly, the capacity and operation of various technologies for electricity generation were determined to supply electricity demand (including electricity demand for heating) with a minimum annualized cost. Then, using the levelized cost of electricity calculated in the power generation mix optimization problem, the optimal heat supply mix was determined through the minimization of annualized cost. The iterative optimization of electricity and heat was continued until an equilibrium was achieved. The results were compared with a centralized optimization model that heat and electricity supply problems are solved simultaneously. • Interaction of the heat and electricity supply systems is modeled and investigated. • A bilevel model is developed and solved using an interactive method. • Heat and electricity supply systems are also solved using a centralized model. • Comparison of two methods show the advantage of bilevel modeling of energy sectors. [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]