A review of computer tools for analysing the integration of renewable energy into various energy systems

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the latest research advances in this field.

Optimization methods applied to renewable and sustainable energy: A review

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA techniques are used to analyze time series and, in the past, have been mainly used for load forecasting, due to their accuracy and mathematical soundness. A detailed explanation of the aforementioned ARIMA models and results from mainland Spain and Californian markets are presented.

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ARIMA models to predict next-day electricity prices

Three very different methods of accomplishing the same task-managing the operation of the transmission system in the deregulated power system operating environment-have been implemented as deregulated market structures have been created around the world. They are first, the optimal power flow (OPF) model found in various implementations in the United Kingdom, parts of the United States, and in Australia and New Zealand. Second, the point tariff, price area congestion control model used in the Nordpool market area in Norway and Sweden. Third, the US transaction-based model. All are pragmatic solutions implemented in advance of complete theoretical understanding. Each has strengths and flaws, and there are some surprising inter-relationships. Each maintains power system security but differs in its impact on the economics of the energy market. No clearly superior method has so far emerged. In the future, methods of combining decentralized market solutions with operational use of optimal power flow may provide better solutions to existing and emerging problems.

Transmission management in the deregulated environment

This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported.

Day-ahead electricity price forecasting using the wavelet transform and ARIMA models

The paper givas supply system in Norway, approach. Procedures ard scheduling are described. Scheduling is carried out is based on profit important factor; this price of the paper describes market price as an how to extend the common this case. Implications to also discussed.

scheduling in a deregulated system. The Norwegian case

The paper gives an overview of the deregulated electricity supply system in Norway, with emphasis on the generation scheduling approach. Procedures and tools for long-, medium- and short-term scheduling are described. Scheduling is carried out by each individual utility. Since scheduling is based on profit maximizing, the spot market price is a very important factor; this price is varying and uncertain, however. A part of the paper describes how to deal with this by considering the spot market price as an exogenously given stochastic variable. It is shown how to extend the common water value computations so as to handle this case. Implications to the medium- and short-term scheduling are also discussed.

Generation scheduling in a deregulated system. The Norwegian case

Optimal investment strategies in decentralized renewable power generation under uncertainty

This paper presents a method for evaluating investments in decentralized renewable power generation under price un certainty. The analysis is applicable for a client with an electricity load and a renewable resource that can be utilized for power generation. The investor has a deferrable opportunity to invest in one local power generating unit, with the objective to maximize the profits from the opportunity. Renewable electricity generation can serve local load when generation and load coincide in time, and surplus power can be exported to the grid. The problem is to find the price intervals and the capacity of the generator at which to invest. Results from a case with wind power generation for an office building suggests it is optimal to wait for higher prices than the net present value break-even price under price uncertainty, and that capacity choice can depend on the current market price and the price volatility. With low price volatility there can be more than one investment price interval for different units with intermediate waiting regions between them. High price volatility increases the value of the investment opportunity, and therefore makes it more attractive to postpone investment until larger units are profitable.

Ivar Wangensteen

Papers

scheduling in a deregulated system. The Norwegian case

Generation scheduling in a deregulated system. The Norwegian case

Optimal investment strategies in decentralized renewable power generation under uncertainty

Optimal investment strategies in decentralized renewable power generation under uncertainty

Hydro reservoir handling in Norway before and after deregulation