Short-term hourly load forecasting using time-series modeling withpeak load estimation capability

This paper presents a new time series modeling for short term load forecasting, which can model the valuable experiences of the expert operators. This approach can accurately forecast the hourly loads of weekdays, as well as, of weekends and public holidays. It is shown that the proposed method can provide more accurate results than the conventional techniques, such as artificial neural networks or Box-Jenkins models. In addition to hourly loads, daily peak load is an important problem for dispatching centers of a power network. Most of the common load forecasting approaches do not consider this problem. It is shown that the proposed method can exactly forecast the daily peak load of a power system. Obtained results from extensive testing on the Iran's power system network confirm the validity of the developed approach     

Market data analysis and short-term price forecasting in the Iran electricity market with pay-as-bid payment mechanism

Market data analysis and short-term price forecasting in Iran electricity market as a market with pay-as-bid payment mechanism has been considered in this paper. The data analysis procedure includes both correlation and predictability analysis of the most important load and price indices. The employed data are the experimental time series from Iran electricity market in its real size and is long enough to make it possible to take properties such as non-stationarity of market into account. For predictability analysis, the bifurcation diagrams and recurrence plots of the data have been investigated. The results of these analyses indicate existence of deterministic chaos in addition to non-stationarity property of the system which implies short-term predictability. In the next step, two artificial neural networks have been developed for forecasting the two price indices in Iran's electricity market. The models’ input sets are selected regarding four aspects: the correlation properties of the available data, the critiques of Iran's electricity market, a proper convergence rate in case of sudden variations in the market price behavior, and the omission of cumulative forecasting errors. The simulation results based on experimental data from Iran electricity market are representative of good performance of the developed neural networks in coping with and forecasting of the market behavior, even in the case of severe volatility in the market price indices.     

Electricity market price spike analysis by a hybrid data model and feature selection technique

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets.     

Reactive power market development considering power system security

In the new open access environment, in pursuit of profit, power producers tend to operate closer to the security boundaries and consequently, the voltage instability, which is caused by insufficient reactive power support, threats system security and reliability. This paper presents a day-ahead reactive power market based on uniform auction price scheme considering voltage security. First, expected payment function (EPF), identified earlier in the literature to construct a bidding framework for synchronous generators, is modified. Then, the modified EPF is used as the objective function of optimal power flow problem to clear reactive power market. Finally, the OPF, including overload, voltage drop and voltage stability margin in its constraints, is solved by binary coded genetic algorithm. The validity of the proposed reactive power market is studied based on the IEEE 24-bus reliability test system.     

Joint market clearing in a stochastic framework considering power system security

This paper presents a new stochastic framework for provision of reserve requirements (spinning and non-spinning reserves) as well as energy in day-ahead simultaneous auctions by pool-based aggregated market scheme. The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte–Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) and simulation studies are carried out to examine the effectiveness of the proposed method.     

Application of a new hybrid neuro-evolutionary system for day-ahead price forecasting of electricity markets

In this paper, a new forecast strategy is proposed for day-ahead prediction of electricity prices, which are so valuable for both producers and consumers in the new competitive electric power markets. However, electricity price has a nonlinear, volatile and time dependent behavior owning many outliers. Our forecast strategy is composed of a preprocessor and a Hybrid Neuro-Evolutionary System (HNES). Preprocessor selects the input features of the HNES according to MRMR (Maximum Relevance Minimum Redundancy) principal. The HNES is composed of three Neural Networks (NN) and Evolutionary Algorithms (EA) in a cascaded structure with a new data flow among its building blocks. The effectiveness of the whole proposed method is demonstrated by means of real data of the PJM and Spanish electricity markets. Also, the proposed price forecast strategy is compared with some of the most recent techniques in the area.     

Energy price forecasting - problems and proposals for such predictions

This paper discusses the value of price forecasting in the electricity market during bidding or hedging against volatility. When bidding in a pool system, the market participants are requested to express their bids in terms of prices and quantities. Since the bids are accepted in order of increasing price until the total demand is met, a company that is able to forecast the pool price can adjust its own price/production schedule depending on hourly pool prices and its own production costs. This paper also discusses the challenges of price forecasting and describes some of the proposed methods for meeting these challenges.     

Stochastic Multiobjective Market Clearing of Joint Energy and Reserves Auctions Ensuring Power System Security

In this paper, a stochastic multiobjective framework is proposed for day-ahead joint market clearing. The proposed multiobjective framework can concurrently optimize competing objective functions including augmented generation offer cost and security indices (overload index, voltage drop index, and voltage stability margin). Besides, system uncertainties including generating units and branches contingencies and load uncertainty are explicitly considered in the stochastic market clearing scheme. The solution methodology consists of two stages, which firstly, employs roulette wheel mechanism and Monte Carlo simulation (MCS) for random adaptive 24-h scenario generation wherein the stochastic multiobjective market clearing procedure is converted into its respective deterministic equivalents (scenarios). In the second stage, for each deterministic scenario, a multiobjective mathematical programming (MMP) formulation based on the epsiv -constrained approach is implemented for provision of spinning reserve (SR) and nonspinning reserve (NSR) as well as energy. The MMP formulation of the market clearing process is optimized while meeting AC power flow constraints and expected interruption cost (EIC). The IEEE 24-bus Reliability Test System (RTS 24-bus) is used to demonstrate the performance of the proposed method.     

A new spinning reserve requirement forecast method for deregulated electricity markets

Ancillary services are necessary for maintaining the security and reliability of power systems and constitute an important part of trade in competitive electricity markets. Spinning Reserve (SR) is one of the most important ancillary services for saving power system stability and integrity in response to contingencies and disturbances that continuously occur in the power systems. Hence, an accurate day-ahead forecast of SR requirement helps the Independent System Operator (ISO) to conduct a reliable and economic operation of the power system. However, SR signal has complex, non-stationary and volatile behavior along the time domain and depends greatly on system load. In this paper, a new hybrid forecast engine is proposed for SR requirement prediction. The proposed forecast engine has an iterative training mechanism composed of Levenberg–Marquadt (LM) learning algorithm and Real Coded Genetic Algorithm (RCGA), implemented on the Multi-Layer Perceptron (MLP) neural network. The proposed forecast methodology is examined by means of real data of Pennsylvania–New Jersey–Maryland (PJM) electricity market and the California ISO (CAISO) controlled grid. The obtained forecast results are presented and compared with those of the other SR forecast methods.     

Evaluation of the maximum loadability point of power systems considering the effect of static load models

Power system stability is an important problem for power system operation. Determination of different stability margins can result in the optimum utilization of power system with minimum risk. Voltage stability is an important subset of power system stability. To correctly analyze the voltage stability of a power system, suitable dynamic models are usually required. However, static analysis tools can give us useful information about long term voltage stability. Especially, maximum loadability point (MLP) of a power system can be effectively estimated by modal analysis of load flow Jacobians. MLP is one of the important boundaries of voltage stability feasible region that loading beyond which is of little practical meaning. In this paper, MLP boundary of power system is analyzed by means of static analysis tools and its differences with the other boundaries of voltage stability, like saddle node bifurcation, are discussed. Effect of reactive power limits of generators and different static load models on the MLP border is also evaluated.