Amir Motamedi

Classification of Future Electricity Market Prices

Forecasting short-term electricity market prices has been the focus of several studies in recent years. Although various approaches have been examined, achieving sufficiently low forecasting errors has not been always possible. However, certain applications, such as demand-side management, do not require exact values for future prices but utilize specific price thresholds as the basis for making short-term scheduling decisions. In this paper, classification of future electricity market prices with respect to pre-specified price thresholds is introduced. Two alternative models based on support vector machines are proposed in a multi-class, multi-step-ahead price classification context. Numerical results are provided for classifying prices in Ontarios and Albertas markets.

Medium-term electricity price forecasting

Medium-term price forecasting plays an important role in electricity markets. Obtaining reasonably accurate medium-term electricity price predictions is critical in many applications, such as maintenance scheduling, generation expansion planning, and bilateral contracting. Medium-term price forecasting is a complex task because of the long forecasting horizon, dependence of medium-term electricity prices to various variables, and limited availability of explanatory data. In this paper, different aspects of a medium-term electricity price forecasting are discussed, and several data-driven 12-month-ahead approaches are developed. The presented approaches are applied to Nord Pool and the Ontario electricity market to examine their performance and effectiveness.

Optimum simultaneous clearing of energy and spinning reserve markets using cost/benefit analysis

Considering the high level of interactions between energy and reserve markets, simultaneous clearing of these markets has been shown to provide more efficient solutions, than a sequential market clearing process. In addition, determining reserve requirements based on probabilistic approaches results in more economical efficiency, while maintaining an acceptable level of reliability, compared to deterministic approaches. This paper proposes a new method for clearing energy and spinning reserve markets simultaneously, while at the same time, spinning reserve requirements are determined based on a cost/benefit analysis as a probabilistic-based approach. This cost/benefit analysis considers both the costs of providing extra reliability and the benefits of fewer customer interruptions to the society. Case studies for modified IEEE reliability test system demonstrate the usefulness and efficiency of the proposed method.

Electricity price thresholding and classification

With the advent of smart grids, electricity consumers are expected to be enabled to respond to electricity price fluctuations. Thus, forecasting short-term future electricity prices is a key component of optimal demand-side management in an enabled competitive environment. While several price forecasting approaches are available, achieving low forecasting errors is not always possible, especially for markets with high price volatility. On the other hand, for demand-side management applications the exact value of future prices is not primarily required, rather, whether prices hit certain thresholds is the basis of making planning decisions. In this paper, classification of future electricity market prices with respect to pre-specified price thresholds is investigated. Two alternative models based on support vector machines are proposed in a multi-class, multi-step-ahead price classification context. Numerical results are provided for price classification in Ontarios and Albertas markets.

Electricity market price forecasting in a price-responsive smart grid environment

In this paper, issues related to price forecasting in a smart grid environment are discussed. In such an environment, demand-side resources are enabled to play a bigger role in the operation of electricity markets, compared to the limited demand-side market participation in most of the existing markets. The response of the demand-side to price forecasts could impact the formation of price patterns, thus calling for further research.

Fuzzy economic dispatch and spinning reserve allocation using evolutionary programming

The objective of economic dispatch (ED) problem is minimizing the operating cost, and finding the point where the total output of the generations equals the total power that must be delivered. In that point the incremental cost of power generation is also equal for all generators. Considering the presence of uncertainty and inaccuracy in the data, fuzzy set theory is an appropriate method to solve the economic dispatch problems. In this paper, a modified fuzzy economic dispatch is formulated to solve the problem under the competitive electricity market uncertainties. Also, available spinning reserve is optimally allocated to market participants. The formulated optimization problem it then solved using the evolutionary programming (EP). The advantage of the proposed approach, in compression to classical method, is shown in one application example.

Ancillary Service Markets

Ancillary services are generally referred as those services other than energy that are essential for ensuring reliable operation of electric power systems. This paper discusses different approaches for procuring required ancillary services for power system security. Furthermore, various design options for ancillary service markets are studied. Such markets are typically operated by an independent system operator (ISO). Different characteristics of each design option are also explained in this paper. The two most important factors which will be discussed for each design option are the price reversal phenomenon and total procurement cost.

Electricity price forecasting considering residual demand

In this paper, short-term electricity price forecasting considering residual electricity demand is investigated. Residual, or net, demand is determined by subtracting any unpredictable generation from the system load. Focusing on wind energy as the main hard-to-predict source of electricity, we first examine the dependency of short-term electricity prices and wind power using data association mining algorithms. Second, we investigate the impact of including net demand in short-term electricity price forecasting, and we propose a new electricity price forecasting model. Data from the Alberta and the Nordic electricity markets are used to conduct studies and evaluate the forecasting results.

Transmission planning in deregulated markets considering GenCos' strategic behavior

This paper proposes a new approach for long-term transmission expansion planning, from the ISOs point of view, in electricity markets where only the generation sector is deregulated. The strategic behavior of generation companies is simulated using the simulated annealing Q-learning technique, and the market operation is modeled. Considering the predicted system data, various likely generation expansion scenarios are evaluated by the system operator, and the most likely scenario is selected according to a predefined objective. Based on this scenario, the ISO evaluates several transmission expansion plans, and the one that maximizes the competition level in the market is selected. A small 5-bus test system is used to demonstrate the proposed approach.