Derk J. Swider

Compressed Air Energy Storage in an Electricity System With Significant Wind Power Generation

In this paper, a stochastic electricity market model is applied to estimate the effects of significant wind power generation on system operation and on economic value of investments in compressed air energy storage (CAES). The models principle is cost minimization by determining the system costs mainly as a function of available generation and transmission capacities, primary energy prices, plant characteristics, and electricity demand. To obtain appropriate estimates, notably reduced efficiencies at part load, start-up costs, and reserve power requirements are taken into account. The latter are endogenously modeled by applying a probabilistic method. The intermittency of wind is covered by a stochastic recombining tree and the system is considered to adapt on increasing wind integration over time by endogenous modeling of investments in selected thermal power plants and CAES. Results for a German case study indicate that CAES can be economic in the case of large-scale wind power deployment

A comparison of empirically based steady-state models for vapor-compression liquid chillers

Abstract This paper presents a comprehensive comparison of empirically based models for steady-state modeling of vapor-compression liquid chillers. Next to the considered models already proposed in the open literature, i.e. regression, thermodynamic, and a radial basis function neural network model, a multilayer perceptron neural network model is introduced. The models predict the coefficient of performance by only using input variables that are readily known to the operating engineer. They are applied to two different chillers operating at the University of Auckland, New Zealand. The comparison demonstrates that neural networks show higher generalization abilities and at least equal forecast results compared to the regression models. Procedures are presented that make models without any physical meaning in the parameters possible to be used in fault detection and diagnosis. It is inferred that black-box models, in particular the radial basis function neural network model, may be preferred for predicting a chiller’s performance in these purposes.

Bidding under price uncertainty in multi-unit pay-as-bid procurement auctions for power systems reserve

In this paper a methodology for profit maximized bidding under price uncertainty in a day-ahead, multi-unit and pay-as-bid procurement auction for power systems reserve is proposed. Within this novel methodology a bidder is considered to follow a Bayes-strategy. Thereby, one bidder is assumed to behave strategically and the behavior of the remaining is summarized in a probability distribution of the market price and a reaction function to price dumping by the strategic bidder. With this approach two problems arise: First, as a pay-as-bid auction is considered, no uniform price and therefore no single probability distribution of the market price is readily available. Second, if historic bidding data of all participants are used to estimate such a distribution and market power is a relevant factor, the bid of the strategically behaving bidder is likely to influence the distribution. Within this paper for both of the problems solutions are presented. It is shown that by estimating a probability of acceptance the optimal bidding price with respect to a given capacity can be calculated by maximizing a stochastic non-linear objective function of expected profit. Taking the characteristics of recently established markets in Germany into account, the methodology is applied using exemplary data. It is shown that the methodology helps to manage existing price uncertainties and hence supports the trading decisions of a bidder. It is inferred that the developed methodology may also be used for bidding on other auction markets with a similar market design.

Model-based long-term electricity generation system planning under uncertainty

Within this paper a stochastic linear mixed-integer model for power plant investment planning under uncertainty is presented. Technical constraints relating to thermal power plants are implemented to better determine their operational flexibility. Modeled with a scenario recombining tree, uncertain wind power generation is explicitly considered in the unit commitment planning. In order to investigate the enhanced impact of heat supply on the electricity market due to increasing capacity of combined heat and power units, district heating systems are incorporated. Modeling results for the German electricity market are discussed regarding the significant influence of uncertainty on investment decisions.

An electricity market model to estimate the marginal value of wind in an adapting system

In this paper a stochastic fundamental electricity market model is presented. The models principle is cost minimization by determining the marginal system costs mainly as a function of available generation and transmission capacities, primary energy prices, plant characteristics and electricity demand. To obtain appropriate estimates of the marginal value of wind in an adapting system notably reduced efficiencies at part load, start-up costs and reserve power requirements are taken into account. The intermittency of wind is covered by a stochastic recombining tree and the system is considered to adapt on increasing wind integration over time by endogenous modeling of investments in thermal power plants. Exemplary results are presented for a German case study

Regional electricity price differences due to intermittent wind power in Germany: impact of extended transmission and storage capacities

A fast growth of installed wind power capacity has been experienced in several European countries. In Germany, notably high wind power capacities are planned in the North. If the existing transmission capacities between individual regions become insufficient, the electricity prices will differ considerably and the system operation can become unstable. It is supposed that the extension of transmission capacities and the use of energy storages avoid this separation of individual regions. In this paper, the arising price differences between regions in Germany are estimated using a novel stochastic linear optimisation model that optimises the efficient power market operation on an hourly basis. To estimate the impact of extensions of transmission lines and energy storages on the electricity prices systematic case studies are undertaken. It is exemplarily shown that differences of electricity prices can be equalised with transmission capacity extensions whereas they remain with the use of storage devices.

Efficient Scoring-Rule in Multipart Procurement Auctions for Power Systems Reserve

In this paper, scoring-rules are analyzed based on the German markets for procuring power system reserves. For paying accepted offers, the pay-as-bid settlement-rule is applied. Each offer consists of a reserve capacity and two prices. The capacity price is for holding the capacity in reserve and the energy price is for delivery in case of actual use. A scoring-rule is applied to value the two-part price bids. Today, this scoring-rule is based on procuring the offers in the rank order of the capacity prices. A first analysis of historic price data indicates efficiency problems due to this scoring-rule. This leads to proposing to value the two-part price bids based on the ex post knowledge of the actual use of the procured reserves. It is shown that in contrast to todays scoring-rule, this duration curve approach gives incentives to reveal the variable generation costs in the energy price bids.