Power System Reliability and Demand Forecasting
Advanced course in quantitative power-system reliability and short-term demand forecasting. It develops probability and stochastic-process foundations, analytical and Monte Carlo reliability methods, single-area, multi-area, and composite-system evaluation, then applies time-series, curve-fitting, Fourier, and ARIMA methods to operational load forecasts.
Objectives
- Define probability, frequency, duration, and cost-based measures of power-system reliability.
- Apply probability rules, random variables, distributions, survival and hazard functions, and Markov processes.
- Construct state-space, transition-rate, frequency-balance, network-reduction, cut-set, and convolution models.
- Evaluate generating-capacity adequacy and spinning-reserve requirements for a single area.
- Analyze interconnected areas with transfer constraints, decomposition methods, and Monte Carlo simulation.
- Evaluate composite generation and transmission reliability at system and load-point levels.
- Identify the operational time horizons, drivers, uncertainty, and error measures relevant to short-term demand forecasting.
- Decompose demand into base, daily, weekly, seasonal, economic, weather, and random components.
- Apply curve fitting, Fourier analysis, FFT, and ARIMA concepts to short-term forecasting.
- Interpret forecast errors and select models appropriate to operating and market decisions.
Modules
Description: Introduces reliability as a measurable system attribute that can be traded against cost and other planning objectives. It develops probability of failure, frequency of failure, mean up and down times, interruption costs, and the use of reliability as a constraint or optimization objective.
Description: Reviews sample spaces, events, conditional and independent probability, combinatorial rules, random variables, probability distributions, survival and hazard functions, exponential models, stochastic processes, Markov processes, and transition probabilities used in reliability calculations.
Description: Develops component and system state models from failure and repair rates. It introduces transition rates, state probabilities, state-transition diagrams, steady-state frequency, and the frequency-balance approach used to calculate reliability indices.
Description: Evaluates generating-capacity adequacy for one area by combining generation and load models. Topics include capacity-outage states, loss-of-load indices, discrete convolution, state rounding, continuous-distribution approximation, and operating-reserve assessment.
Description: Extends adequacy evaluation to interconnected areas with finite transfer capability. It develops area-assistance models, flow constraints, acceptable and loss-of-load state sets, decomposition methods, radial equivalents, and N-area simulation concepts.
Description: Introduces nonsequential random sampling and time-sequential Monte Carlo approaches for large reliability models. It explains state sampling, component availability, convergence, index estimation, chronological behavior, and the tradeoffs between simulation and analytical methods.
Description: Evaluates the combined ability of generation and transmission to serve system load points. It introduces network-state modeling, power-flow feasibility, transmission constraints, load curtailment, system and bus reliability indices, and decomposition techniques for large composite systems.
Description: Places demand forecasting within immediate, short-, medium-, and long-term operational planning. It distinguishes demand, losses, prices, and ancillary services and introduces the weather, calendar, customer, economic, and random influences that shape hourly load.
Description: Surveys qualitative and quantitative forecasting approaches, including expert judgment, time series, regression, filtering, ARMA and ARIMA models, and artificial neural networks. It frames system identification, parameter estimation, and the separation of signal from noise.
Description: Connects forecast production and error analysis to unit scheduling, economic dispatch, energy storage, demand response, market offers, and energy-imbalance decisions. It emphasizes choosing the required horizon and accuracy for each operational use.
Description: Decomposes recurring load patterns into base, weekly, seasonal, economic, weather, distributed-supply, and noise components. It develops error metrics, moving averages, regression and curve-fitting concepts, model order, outlier treatment, and parameter estimation.
Description: Applies Fourier and time-series methods to recurring demand patterns. Topics include DFT and FFT spectra, periodic components, aliasing and leakage, preprocessing for bias and trend, autocorrelation, stationarity, differencing, and autoregressive integrated moving-average models.