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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]

Accurate information regarding the uncertainty of short-term forecast for aggregate wind power is a key to efficient and cost effective integration of wind farms into power systems. This paper presents a methodology for producing wind power forecast scenarios. Using historical wind power time series data and the Kernel Density Estimator (KDE), probabilistic wind power forecast scenarios were generated according to a rolling process. The improvement achieved in the accuracy of forecasts through frequent updating of the forecasts taking into account the latest realized wind power was quantified. The forecast scenarios produced by the proposed method were used as inputs to a unit commitment and optimal dispatch model in order to investigate how the uncertainty in wind forecast affect the operation of power system and in particular gas-fired generators. [ABSTRACT FROM AUTHOR]

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 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]

• 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]