The integration of microgrids and the combined cooling heating and power (CCHP) systems can foster a better utilization of energy. In order to achieve economic optimization and peak-load reduction of ...the CCHP microgrids model, this paper proposes a multi-objective optimal scheduling model for CCHP microgrids integrated with renewable energy, energy storage system and incentive based demand response. First, linearization methods are applied to change the original nonlinear optimization model into a mixed-integer linear programming (MILP) problem. Then, an augmented ε-constraint (AUGMECON) method is implemented to solve the multi-objective optimization problem (MOP). Finally, the final scheme is selected from the obtained Pareto optimal set by fuzzy clustering method according to the preference of decision maker. The results show that the CCHP microgrids is effective in reducing pollutant gas emissions and reducing the cost of treating them. And compared with the other four intelligent algorithms, the proposed MILP method has better accuracy and computational efficiency. In addition, with the inclusion of the peak-load shifting function, the interruptible load and the battery can effectively respond to peak load changes by shifting the peak of the exchange power curve in the point of common coupling (PCC) of the CCHP microgrids. In the end, the sensitivity analysis is carried out and the results present that electricity price, natural gas price, and the efficiency of PV have varying degrees of impact on model performance.
•A multi-objective optimization model including economy and load shifting is built.•Turning the model into a mixed integer linear programming problem.•Multi-objective optimization problem solved with an augmented ε-constraint method.•Optimal scheduling is realized in terms of accuracy and computation time.
The objective of this study is to propose a decision-tree-based peak shaving algorithm for islanded microgrid. The proposed algorithm helps an islanded microgrid to operate its generation units ...efficiently. Effectiveness of the proposed algorithm was tested with a BESS-based MATLAB/Simulink model of an actual microgrid under realistic load conditions which were recorded. To evaluate the performance, simulation case studies were conducted under various load conditions and results were compared with conventional techniques. Results showed that the proposed algorithm offers a simple and effective way of peak load shaving without heavy computational burdens often needed in other methods. The comparison analysis verified that the proposed algorithm can effectively mitigate the peak load demand regardless of the schedule of the generators, where conventional methods were limited. The financial benefit investigation shows that microgrid utility can enjoy substantial savings, while reducing of the peak demand of the microgrid. Thus, the islanded microgrid that include fuel-based generation can implement the proposed technique to reduce the consumption of fuel and increase the efficiency of fuel-based generation through peak load mitigation.
•A novel peak shaving algorithm is presented for the islanded microgrid.•The proposed algorithm is tested for a real microgrid under various load conditions.•A comparative analysis is carried out with conventional peak shaving methods.•Results show the proposed algorithm successfully supplied peak shaving services.•The proposed algorithm shows a robust behavior under the realistic circumstance.
•A cost-emission model as energy hub system is investigated for industrial consumer.•Heat and power hub model is proposed to supply power and heat demands.•Compromise programming is proposed to solve ...model and obtain Pareto solutions.•The trade-off solution is selected by fuzzy decision making approach.•Peak load management is proposed in order to reduce cost and emission.
In addition to economic issue, an emission issue should also be considered in the operation of an industrial consumer in order to reduce greenhouse gases like NO2, SO2 and CO2 to the atmosphere. Also, multi-carrier energy hub system can be used to supply heat and power demand by an industrial consumer. Therefore, this paper proposes a conflict bi-objective model for cost-emission based operation of industrial consumer in the presence of peak load management. Compromise programming is proposed to solve the proposed bi-objective model in order to obtain the Pareto solutions. Furthermore, fuzzy decision making approach is provided to select the trade-off solution from the Pareto solutions. Finally, peak load management is employed to flat the load profile in order to reduce the operation cost and emission. The proposed model is formulated as a mixed-integer linear program which is solved by using CPLEX solver in the GAMS optimization software. Two case studies have been used and obtained results are compared to validate the performance of proposed model.
The incorporation of phase change materials (PCMs) in buildings increases their thermal mass and hence improves thermal comfort through internal temperature stabilization. In this paper, the thermal ...performance of an active PCM system was compared with that of a passive system. Two identical test huts, each equipped with a control system were used to investigate the potential of passive and active systems for energy-saving and peak load shifting. One of the huts was equipped with PCM-impregnated wallboards, while the other hut was provided with active air-PCM heat storage units designed and fabricated at the University of Auckland. Both huts were cooled with an air conditioner or heated using both solar and electric heaters. For space cooling, the hut with active PCM consumed 8% more electricity to maintain comfort, although the energy storage capacity of PCM used was 50% less than that used in the active application. Over ten days in winter, the energy consumed in the hut provided with an active storage system was 22% less when both passive and active systems had the same amount of energy storage capacity. The investigation of peak load shifting showed 32% less in electricity cost when an active storage system was used.
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•The performance of an active PCM system was compared with that of a passive system.•Active system showed a greater energy-saving and a more efficient peak load shifting.•32% less electricity cost was achieved when an active storage system was used.•Active systems provide better level of control, and less adverse environmental impact.
The treatment of trend components in electricity demand is critical for long-term peak load forecasting. When forecasting high frequency variables, like daily or hourly loads, a typical problem is ...how to make long-term scenarios - regarding demographics, GDP growth, etc. - compatible with short-term projections. Traditional procedures that apply de-trending methods are unable to simulate forecasts under alternative long-term scenarios. On the other hand, existing models that allow for changes in long-term trends tend to be characterized by end-of-year discontinuities. In this paper a novel forecasting procedure is presented that improves upon these approaches and is able to combine long and short-term features by employing temporal disaggregation techniques. This method is applied to forecast electricity load for Spain and its performance is compared to that of a nonlinear autoregressive neural network with exogenous inputs. Our proposed procedure is flexible enough to be applied to different scenarios based on alternative assumptions regarding both long-term trends as well as short-term projections.
•Grid capacity planning is critically linked to peak demand forecasts.•A methodology to produce long-term hourly peak load forecasting is presented.•Modelling hourly load have to deal with long-term and short-term features.•Long and short-term features are combined by temporal disaggregation techniques.•The method is flexible and allows for what-if simulations, key in grid planning.
Thermal Energy Storage (TES) has been a topic of research for quite some time and has proven to be a technology that can have positive effects on the energy efficiency of a building by contributing ...to an increased share of renewable energy and/or reduction in energy demand or peak loads for both heating and cooling. There are many TES technologies available, both commercial and emerging, and the amount of published literature on the subject is considerable. Literature discussing the combination of thermal energy storage with buildings is however lacking and it is therefore not an easy task to decide which type of TES to use in a certain building. The goal of this paper is to give a comprehensive review of a wide variety of TES technologies, with a clear focus on the combination of storage technology and building type. The results show many promising TES technologies, both for residential and commercial buildings, but also that much research still is required, especially in the fields of phase change materials and thermochemical storage.
Load forecasting is one of the main required studies for power system expansion planning and operation. In order to capture the nonlinear and complex pattern in yearly peak load and energy demand ...data, a hybrid long term forecasting method based on data mining technique and Time Series is proposed. First, a forecasting algorithm based on the Support Vector Regression (SVR) method is developed. The parameters of the SVR technique along with the dimension of input samples are optimized using a Particle Swarm Optimization (PSO) method. Secondly, in order to minimize the forecasting error, a hybrid forecasting method is presented for long term yearly electric peak load and total electric energy demand. The proposed hybrid method acts based on the combination of Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and the proposed Support Vector Regression technique. The parameters of the ARIMA method are determined based on the autocorrelation and partial autocorrelation of the original and differenced time series. The proposed hybrid forecasting method prioritizes each forecasting method based on the resulted error over the existing data. The hybrid forecasting method is used to forecast the yearly peak load and total energy demand of Iran National Electric Energy System.
•Proposing an SVR algorithm to forecast the long term load and energy demands.•Utilizing Particle Swarm Optimization to optimize the parameters of SVR algorithm.•Proposing a hybrid forecasting model using the proposed PSO-SVR and other methods.•Implementing the proposed methods in the real life case of Iran national grid.
Air-conditioning systems in commercial buildings are usually switched on before office hour to precool buildings to create an acceptable working environment at the beginning of the office hour in ...cooling seasons. However, due to high cooling demand during morning start period particularly in hot seasons, often much higher than the capacity of cooling supply, the feedback control strategies in air-conditioning systems often fail to control this cooling process properly. The imbalanced cooling distribution and large difference of cooling-down speeds among different spaces result in the need of significantly extended precooling duration as well as over-speeding of water pumps and fans that lead to serious energy waste and high peak demand. An optimal control strategy is therefore developed to determine the number and schedule of operating chillers and particularly to achieve an optimal cooling distribution among individual spaces. Case studies are conducted and results show that the proposed control strategy could shorten the precooling time about half an hour because of similar cooling-down speeds among individual zones. The energy consumption of the air-conditioning system during morning start period is also reduced over 50%. In addition, the peak demand is reduced significantly contributed by the improved controls of secondary pumps and fans.
•On-site data are used to elaborate the energy and control problems during morning start period.•An optimal control strategy for air-conditioning systems is proposed for morning start period.•Supply-based feedback control achieves even cooling of spaces effectively under limited supply.•Precooling time is shortened significantly by optimized cooling distribution.•Energy saving is about 50% during morning start period (4.5% of total A/C system consumption).
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.
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