Swiss pumped hydro storage potential for Germany’s electricity system under high penetration of intermittent renewable energy

In order to cut greenhouse-gas emissions and increase energy security,the European Commission stimulates the deployment of intermittent renewable energy sources(IRES) towards 2050.In an electricity system with high shares of IRES implemented in the network,energy balancing like storage is needed to secure grid stability and smooth demand satisfaction.Pumped hydro storage(PHS) is at this moment the best option for large scale storage.Switzerland has strong ambitions to further develop their PHS sector and become the battery of Europe.In this research,the potential of the Swiss PSH plants is explored,whilst taking inflow into the upper reservoirs of the PHS plants into consideration.To simulate electricity imbalance,Germany is used as a case study.Germany already has a high penetration of IRES and has plans to increase installed IRES capacity.By using an energy planning model(Power Plan),three future scenarios of the German electricity system were designed,each with a different set of IRES installed(solar,mixed and wind).Results show that the Swiss battery ambition offers most benefits to a wind-oriented scenario,reducing both shortages as well as surpluses.Water inflow in Swiss PHS-reservoirs is of minor importance when looking at security of supply,although it was shown that the solarscenario profits more from inflow in terms of system stability.However,a potential conflict was observed in the solar-scenario between the need for electricity storage and the storage of natural inflow,resulting in more surpluses in the system when inflow was taken into account.

Distributed generator-based distribution system service restoration strategy and model-free control methods

The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular interest to utility companies,but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled.In this study,we investigate distribution system service restoration using DGs as the primary power source,and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions.The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations.The uncertainty of renewable DGs will be modeled using a chance-constrained approach.Furthermore,the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output.The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.

Regulating reserve with large penetration of renewable energy using midterm dynamic simulation

A novel renewable energy intermittency model and a new midterm dynamic simulation tool in power systems are developed for examining dynamic behavior along the load curve for different combinations of the system operation reserves and renewable portfolio standard(RPS)rates.The system’s import limits are considered.It is concluded that ignoring intermittency and governor effects is an inadequate method to assess intermittency impact.The intermittency midterm dynamic impact must be studied.For the studied system,the instability is expected to be about 25%RPS with current reserves.Besides,the most vulnerable peak hour to instability is the afternoon peak hour when solar begins to drop off.This article stimulates further dynamic intermittency studies on the issues caused by renewable intermittency.The studies on the issues caused by renewable intermittency have not been revealed because of inadequate/incomprehensive study methodologies so that effective,mitigative solutions can be developed to guarantee the reliability of power grid when incorporating higher RPS if high operation reserves are impractical.

Feature selection for energy system modeling: Identification of relevant time series information

Heuristic or clustering based time series aggregation methods are often used to reduce temporal complexity of energy system models by selecting representative days.However,these methods potentially neglect relevant information of time series(e.g.,distribution parameters).To identify relevant time series parameters,feature selection algorithms can be applied.The present research contributes by(a)developing a new feature selection approach based on clustering,nested modeling and regression(CNR)which is designed for applications requiring high selectivity and using different data sets,(b)comparing and evaluating CNR with feature selection methods available from the literature(e.g.,LASSO)and(c)identifying relevant information of the time series applied in energy system models,in particular those of demand,photovoltaic and wind.Results show that CNR achieves on average up to 101%lower mean absolute errors when methods are directly compared.Thus,CNR better identifies relevant information when the number of selected features is restricted.The disadvantage of CNR,however,is its high computational effort.A potential remedy to counter this is the combination with another method(e.g.,as pre-feature selection).In terms of relevant information,energy systems including photovoltaic are mainly characterized by the correlation between demand and photovoltaic time series as well as the range and the 35%quantile of demand.When energy systems include wind power,the minimum and mean of wind as well as the correlation between demand and wind time series are relevant characteristics.The implications of these findings are discussed.