As the volatility of electricity demand increases owing to climate change and electrification, the importance of accurate peak load forecasting is increasing. Traditional peak load forecasting has ...been conducted through time series-based models; however, recently, new models based on machine or deep learning are being introduced. This study performs a comparative analysis to determine the most accurate peak load-forecasting model for Korea, by comparing the performance of time series, machine learning, and hybrid models. Seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) is used for the time series model. Artificial neural network (ANN), support vector regression (SVR), and long short-term memory (LSTM) are used for the machine learning models. SARIMAX-ANN, SARIMAX-SVR, and SARIMAX-LSTM are used for the hybrid models. The results indicate that the hybrid models exhibit significant improvement over the SARIMAX model. The LSTM-based models outperformed the others; the single and hybrid LSTM models did not exhibit a significant performance difference. In the case of Korea's highest peak load in 2019, the predictive power of the LSTM model proved to be greater than that of the SARIMAX-LSTM model. The LSTM, SARIMAX-SVR, and SARIMAX-LSTM models outperformed the current time series-based forecasting model used in Korea. Thus, Korea's peak load-forecasting performance can be improved by including machine learning or hybrid models.
•We perform a comparative analysis for daily peak load forecasting models.•We compare the performance of time series, machine learning, and hybrid models.•Hybrid models show significant improvements over the traditional time series model.•Single and hybrid LSTM models show no significant performance differences.
Accurate day-ahead peak load forecasting is crucial not only for power dispatching but also has a great interest to investors and energy policy maker as well as government. Literature reveals that 1% ...error drop of forecast can reduce 10 million pounds operational cost. Thus, this study proposed a novel hybrid predictive model built upon multivariate empirical mode decomposition (MEMD) and support vector regression (SVR) with parameters optimized by particle swarm optimization (PSO), which is able to capture precise electricity peak load. The novelty of this study mainly comes from the application of MEMD, which enables the multivariate data decomposition to effectively extract inherent information among relevant variables at different time frequency during the deterioration of multivariate over time. Two real-world load data sets from the New South Wales (NSW) and the Victoria (VIC) in Australia have been considered to verify the superiority of the proposed MEMD-PSO-SVR hybrid model. The quantitative and comprehensive assessments are performed, and the results indicate that the proposed MEMD-PSO-SVR method is a promising alternative for day-ahead electricity peak load forecasting.
•Day-ahead peak load forecasting is implemented with multiple input variables.•A novel hybrid method has been proposed based on MEMD-PSO-SVR.•MEMD approach in multivariate data decomposition prevents information loss.•PSO is utilized to optimize the SVR parameters.•Power planer can use this new hybrid model for day-ahead peak load forecasting.
•Little study reviews the load shifting control using different facilities.•This study reviews load shifting control using building thermal mass.•This study reviews load shifting control using ...thermal energy storage systems.•This study reviews load shifting control using phase change material.•Efforts for developing more applicable load shifting control are addressed.
For decades, load shifting control, one of most effective peak demand management methods, has attracted increasing attentions from both researchers and engineers. Different load shifting control strategies have been developed when diverse cold thermal energy storage facilities are used in commercial buildings. The facilities include building thermal mass (BTM), thermal energy storage system (TES) and phase change material (PCM). Little study has systematically reviewed these load shifting control strategies and therefore this study presents a comprehensive review of peak load shifting control strategies using these thermal energy storage facilities in commercial buildings. The research and applications of the load shifting control strategies are presented and discussed. The further efforts needed for developing more applicable load shifting control strategies using the facilities are also addressed.
Abstract Distributed energy storages (ESs) will be widely used in the future smart microgrids due to their continuous technology improvement and cost reduction. Meanwhile, the distributed control ...method also gains increasing attention for controlling distributed devices to protect user privacy and reduce computation burden compared with the centralized scheme. In this paper, a distributed control method of ESs is proposed for multi-time-step peak load shaving in a microgrid. Considering the ES efficiency is related to its power, an optimization is constructed to minimize the power loss during ES operations when performing peak load shaving function. By analyzing the net load curve and the ES available capacity, the multi-time-step peak load shaving problem is transformed into single-time-step ES operations at each time step. The Combine-Then-Adapt diffusion strategy is united with the consensus + innovation strategy to realize the peak load shaving in a fully distributed way. Case studies verify the efficiency of the proposed method.
Capacity costs of renewable energies have been decreasing dramatically and are expected to fall further, making them more competitive with fossils. Building on an analytically tractable peak-load ...pricing model, we analyze how intermittency of renewable energies affects the market diffusion that results from these lower costs. In particular, once renewables have become competitive by attaining the same levelized cost of electricity (LCOE) as fossils, the marginal increase in efficient capacities due to a further cost reduction varies substantially. Initially it is small, then it rises, but it falls again once renewable capacities are large enough to satisfy the whole electricity demand at times of high availability. If external costs of fossils are internalized by a Pigouvian tax, then perfect competition leads to efficient investments in renewable and fossil capacities; even though we assume that only a subgroup of consumers can adapt their demand to price fluctuations that are caused by the intermittency of renewables. Moreover, fossils receive a capacity payment through the market for their reliability in serving demand of non-reactive consumers. Maximum electricity prices rise with the share of renewables. If regulators impose a price cap, this initially raises investments in renewables, but the effect may reverse if the share of renewables is large.
•Integration of reactive and non-reactive consumers in peak-load pricing model with intermittent renewable energies•S-shaped pattern of efficient market diffusion of renewable energies as they get cheaper•Cheaper renewable energies may raise the efficient level of fossil capacities.•A cap on electricity prices initially raises investments in renewables, but the effect may reverse if the share of renewables is large.•Fossils receive a capacity payment through the market for their reliability in serving demand of non-reactive consumers.
Long-term load forecasting (LTLF) models play an important role in the strategic planning of power systems around the globe. Obtaining correct decisions on power network expansions or restrictions ...based on predictions help substantially reduce the power grid infrastructure costs. The classical approach of LTLF is limited to the usage of artificial neural networks (ANN) or regression-based approaches along with a large set of historical electricity load, weather, economy and population data. Considering the drawbacks of classical methods, this paper introduces a novel sequence to sequence hybrid convolutional neural network and long short-term memory (CNN-LSTM) model to forecast the monthly peak load for a time horizon of three years. These drawbacks include, lack of sensitivity to changing trends over long time horizons, difficulty of fitting large number of variables and complex relationships, etc. 1. Forecasting time interval plays a key role in LTLF. Therefore, using monthly peak load avoids unnecessary complications while providing all essential information for a good long-term strategical planning. The accuracy of the proposed method is verified by the load data of "New South Wales (NSW)", Australia. The numerical results show that, proposed method has achieved higher prediction accuracy compared to the existing work on long-term load forecasting.
A novel peak load shaving algorithm has been proposed in this study for peak shaving application in hybrid PV-BESS connected Isolated Microgrid (IMG) system. This algorithm will help an IMG system to ...operate its generation systems optimally and economically along with PV generation unit. An IMG model has been developed in MATLAB/Simulink environment with actual load data, conventional Gas Turbine Generator (GTG), Photovoltaic (PV) generation system and Battery Energy Storage System (BESS). The proposed algorithm has been tested with the designed IMG model. To evaluate the effectiveness of the algorithm, simulation case studies have been conducted with actual load data and actual PV generation data. The simulation results demonstrate that the algorithm can minimize the limitations of the existing methods and it can use the PV generation system effectively. Peak shaving service can be found from this algorithm as a simple and forcible way. A comparative analysis has been conducted of the proposed algorithm with the conventional methods. The comparison results can easily reflect that the proposed algorithm can ensure the optimal use of PV generation system and can serve peak shaving service effectively. It can also ensure an economic and environment friendly system where the existing algorithms are limited to serve these. The proposed algorithm can mitigate the available power issues for hybrid PV-BESS connected system and it can also minimize the operating cost by dispatching the generation units optimally.
•A novel peak shaving algorithm is presented for isolated microgrid system•The proposed algorithm is tested in a microgrid under various load conditions and PV generations.•Result shows that the proposed algorithm can successfully provide peak shaving services.•A comparative analysis and economic benefit analysis reveal the improve and economic performance.•The proposed algorithm shows a robust behavior under the realistic circumstance.
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