Optimal Dispatching of Smart Hybrid Energy Systems for Addressing a Low-Carbon Community
Abstract
:1. Introduction
2. Smart Hybrid Energy System
3. Forecasting
3.1. Forecast Models
- (i)
- Linear regression (LR): LR is denoted as a forecast model which is directly described by
- (ii)
- Logistic regression (LogR): LogR is denoted as a forecast model in which the function is described by
- (iii)
- Feedforward neural network (FNN): A three-layer FNN is described by [26]
- (iv)
- Recurrent neural network (RNN): yRNN in the discrete-time settings is described by [27]
- (v)
- Nonlinear autoregressive exogenous model (NARX): This model relates the current and past values of the time series of input and outputs as described by [28]Notably, represents the error term due to disturbances, and f is some nonlinear functions such as neural network, sigmoid function, and so on. Moreover, Bayesian optimization is implemented to optimize the hyperparameters of NARX and ensure the validation accuracy of NARX.
- (vi)
- Gaussian process regression (GPR): yGPR is usually described by [29]
- (vii)
- Support vector machine (SVM): SVM is a supervised learning algorithm. ySVM classifies data by finding the best hyperplane [30]Moreover, Bayesian optimization is utilized to adjust the parameters of the SVM classifier and improve the validation accuracy of SVM.
- (viii)
- Random forest (RF) [31]: RF is an ensemble learning method for classification and regression. yRF is evaluated through three steps: (i) A decision tree using all the features/variables of interest as an entire dataset; (ii) Bagging is used to reduce the variance of a decision tree; (iii) The random subspace method for constructing decision forests. Moreover, Bayesian optimization is utilized to adjust the parameters of the RF model and improve the validation accuracy of RF.
- (ix)
- Extreme gradient boosting (XGBoost) [32]: XGBoost is a decision-tree-based ensemble machine learning algorithm that uses a gradient boosting framework. yXGBoost is evaluated through a combination of software and hardware optimization techniques to yield superior results using fewer computing resources in the shortest amount of time. Moreover, Bayesian optimization is utilized to adjust the parameters of XGBoost and improve the validation accuracy of XGBoost.
- (x)
- Long short-term memory (LSTM): LSTM is a recurrent neural network (RNN) architecture used in the field of deep learning. yLSTM is evaluated by [33]
3.2. Forecasting Algorithm
4. Optimal Power Dispatch Strategy
4.1. Operating Reserve
4.2. Optimization
- (i)
- The grid-connected mode:OrThe islanded mode:
- (ii)
- The upper and lower bounds of power units
5. Results and Discussion
5.1. Daily Forecasting Comparisons
- (a)
- In the grid-connected mode, the main grid (gray bar) in Figure 6a,c dominates the main power supply due to limits of energy storage capacity and intermittent solar energy. Due to the main grid with lower LBMP, conventional gas/diesel engines are absent. Using the environmental dispatch strategy by solving EEDOA with w = 0, the power supply from the battery shown in Figure 6c is higher than in Figure 6a, such that the corresponding SOC in Figure 6d is lower than in Figure 6b.
- (b)
- In the islanded mode, the gas turbine (orange bar) and diesel engine (gray bar) in Figure 7a,c become the main power supplies due to no main grid. Using the economic dispatch strategy by solving EEDOA with w=1, the diesel consumption (diesel engine) in Figure 7a is higher than in Figure 7c, such that the corresponding SOC in Figure 7b is higher than in Figure 7d.
5.2. Monthly Forecasting Comparisons
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
EEDOA | Economic/environmental dispatch optimization algorithm |
FNN | Feedforward neural network |
GPQR | Gaussian process quantile regression |
GPR | Gaussian process regression |
LBMP | Locational-based marginal pricing |
LOLP | Loss-of-load probability |
LogR | Logistic regression |
LR | Linear regression |
LSTM | Long short-term memory |
MASE | Mean absolute scaled error |
NARX | Nonlinear autoregressive exogenous model |
OR | Operating reserve |
RF | Random forest |
RNN | Recurrent neural network |
SHES | Smart hybrid energy system |
SVM | Support vector machine |
SOC | State-of-charge |
XGBoost | Extreme gradient boosting |
CT | Total operating cost, $/kWh |
operating costs of gas engine and diesel engines, respectively, $/kWh | |
Pge | Gas engine power, kWh |
Pde | Diesel engine power, kWh |
Psb | Rechargeable battery, kWh |
Pmg | Main grid, kWh |
ηcon | Converter efficiency, % |
ηinv | Inverter efficiency, % |
ηge | Electrical generator efficiency, % |
Regression coefficient | |
Standard logistic function | |
, | Activation functions in hidden layer and output layer, respectively |
, ,, | Weights in FNN and RNN |
npdf | Normal probability density function |
Forecasting errors of load demand and PV power, respectively, kWh | |
Real-time and forecasting load demand, respectively, kWh | |
, | Net real-time and net forecasted demand, respectively, kWh |
Probability |
References
- Bartolini, A.; Carducci, F.; Muñoz, C.B.; Comodi, G. Energy Storage and Multi Energy Systems in Local Energy Communities with High Renewable Energy Penetration. Renew. Energy 2020, 159, 595–609. [Google Scholar] [CrossRef]
- Gil, G.O.; Chowdhury, J.I.; Balta-Ozkan, N.; Hu, Y.; Varga, L.; Hart, P. Optimising Renewable Energy Integration in New Housing Developments with Low Carbon Technologies. Renew. Energy 2021, 169, 527–540. [Google Scholar] [CrossRef]
- Groppi, D.; Astiaso Garcia, D.; LoBasso, G.; DeSantoli, L. Synergy between Smart Energy Systems Simulation Tools for Greening Small Mediterranean Islands. Renew. Energy 2019, 135, 515–524. [Google Scholar] [CrossRef]
- Chen, X.; Xiao, J.; Yuan, J.; Xiao, Z.; Gang, W. Application and Performance Analysis of 100% Renewable Energy Systems Serving Low-Density Communities. Renew. Energy 2021, 176, 433–446. [Google Scholar] [CrossRef]
- Zia, M.F.; Elbouchikhi, E.; Benbouzid, M. Microgrids Energy Management Systems: A Critical Review on Methods, Solutions, and Prospects. Appl. Energy 2018, 222, 1033–1055. [Google Scholar] [CrossRef]
- Liu, Z.; Zhang, H.; Dong, J.; Yu, H. A Prediction-Based Optimization Strategy to Balance the Use of Diesel Generator and Emergency Battery in the Microgrid. Int. J. Energy Res. 2020, 44, 5425–5440. [Google Scholar] [CrossRef]
- Osório, G.J.; Lujano-Rojas, J.M.; Matias, J.C.O.; Catalão, J.P.S. A Probabilistic Approach to Solve the Economic Dispatch Problem with Intermittent Renewable Energy Sources. Energy 2015, 82, 949–959. [Google Scholar] [CrossRef]
- Khan, N.A.; Sidhu, G.A.S.; Awan, A.B.; Ali, Z.; Mahmood, A. Modeling and Operation Optimization of RE Integrated Microgrids Considering Economic, Energy, and Environmental Aspects. Int. J. Energy Res. 2019, 43, 6721–6739. [Google Scholar] [CrossRef]
- Karim, M.A.; Currie, J.; Lie, T.T. A Machine Learning Based Optimized Energy Dispatching Scheme for Restoring a Hybrid Microgrid. Electr. Power Syst. Res. 2018, 155, 206–215. [Google Scholar] [CrossRef]
- Nagapurkar, P.; Smith, J.D. Techno-Economic Optimization and Social Costs Assessment of Microgrid-Conventional Grid Integration Using Genetic Algorithm and Artificial Neural Networks: A Case Study for Two US Cities. J. Clean. Prod. 2019, 229, 552–569. [Google Scholar] [CrossRef]
- Izidio, D.M.; de Mattos Neto, P.S.; Barbosa, L.; de Oliveira, J.F.; Marinho, M.H.; Rissi, G.F. Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters. Energies 2021, 14, 1794. [Google Scholar] [CrossRef]
- Xia, C.; Wang, J.; McMenemy, K. Short, Medium and Long Term Load Forecasting Model and Virtual Load Forecaster Based on Radial Basis Function Neural Networks. Int. J. Electr. Power Energy Syst. 2010, 32, 743–750. [Google Scholar] [CrossRef]
- Bedi, J.; Toshniwal, D. Deep Learning Framework to Forecast Electricity Demand. Appl. Energy 2019, 238, 1312–1326. [Google Scholar] [CrossRef]
- Ribeiro, G.T.; Mariani, V.C.; dos Santos Coelho, L. Enhanced Ensemble Structures Using Wavelet Neural Networks Applied to Short-Term Load Forecasting. Eng. Appl. Artif. Intell. 2019, 82, 272–281. [Google Scholar] [CrossRef]
- Yan, X.; Abbes, D.; Francois, B. Uncertainty Analysis for Day Ahead Power Reserve Quantification in an Urban Microgrid Including PV Generators. Renew. Energy 2017, 106, 288–297. [Google Scholar] [CrossRef]
- Alvarado-Barrios, L.; Rodríguez del Nozal, Á.; Boza Valerino, J.; García Vera, I.; Martínez-Ramos, J.L. Stochastic Unit Commitment in Microgrids: Influence of the Load Forecasting Error and the Availability of Energy Storage. Renew. Energy 2020, 146, 2060–2069. [Google Scholar] [CrossRef]
- Wang, H.Z.; Li, G.Q.; Wang, G.B.; Peng, J.C.; Jiang, H.; Liu, Y.T. Deep Learning Based Ensemble Approach for Probabilistic Wind Power Forecasting. Appl. Energy 2017, 188, 56–70. [Google Scholar] [CrossRef]
- Ahmad, M.W.; Mourshed, M.; Rezgui, Y. Tree-Based Ensemble Methods for Predicting PV Power Generation and Their Comparison with Support Vector Regression. Energy 2018, 164, 465–474. [Google Scholar] [CrossRef]
- Wang, G.; Jia, R.; Liu, J.; Zhang, H. A Hybrid Wind Power Forecasting Approach Based on Bayesian Model Averaging and Ensemble Learning. Renew. Energy 2020, 145, 2426–2434. [Google Scholar] [CrossRef]
- Yang, Y.; Li, S.; Li, W.; Qu, M. Power Load Probability Density Forecasting Using Gaussian Process Quantile Regression. Appl. Energy 2018, 213, 499–509. [Google Scholar] [CrossRef]
- Wen, L.; Zhou, K.; Yang, S.; Lu, X. Optimal Load Dispatch of Community Microgrid with Deep Learning Based Solar Power and Load Forecasting. Energy 2019, 171, 1053–1065. [Google Scholar] [CrossRef]
- Parag, Y.; Ainspan, M. Sustainable Microgrids: Economic, Environmental and Social Costs and Benefits of Microgrid Deployment. Energy Sustain. Dev. 2019, 52, 72–81. [Google Scholar] [CrossRef]
- Moradi, H.; Esfahanian, M.; Abtahi, A.; Zilouchian, A. Optimization and Energy Management of a Standalone Hybrid Microgrid in the Presence of Battery Storage System. Energy 2018, 147, 226–238. [Google Scholar] [CrossRef]
- Chen, Y.; Deng, C.; Li, D.; Chen, M. Quantifying Cumulative Effects of Stochastic Forecast Errors of Renewable Energy Generation on Energy Storage SOC and Application of Hybrid-MPC Approach to Microgrid. Int. J. Electr. Power Energy Syst. 2020, 117, 105710. [Google Scholar] [CrossRef]
- Logistic Regression—A Complete Tutorial with Examples in R. Available online: https://www.machinelearningplus.com/machine-learning/logistic-regression-tutorial-examples-r/ (accessed on 20 June 2022).
- Deep Learning: Feed Forward Neural Networks (FFNNs) by Mohammed Terry-Jack Medium. Available online: https://medium.com/@b.terryjack/introduction-to-deep-learning-feed-forward-neural-networks-ffnns-a-k-a-c688d83a309d (accessed on 20 June 2022).
- Simple Explanation of Recurrent Neural Network (RNN) by Omar Boufeloussen The Startup Medium. Available online: https://medium.com/swlh/simple-explanation-of-recurrent-neural-network-rnn-1285749cc363 (accessed on 20 June 2022).
- Boussaada, Z.; Curea, O.; Remaci, A.; Camblong, H.; Bellaaj, N.M. A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation. Energies 2018, 11, 11030620. [Google Scholar] [CrossRef]
- Schulz, E.; Speekenbrink, M.; Krause, A. A Tutorial on Gaussian Process Regression: Modelling, Exploring, and Exploiting Functions. J. Math. Psychol. 2018, 85, 1–16. [Google Scholar] [CrossRef]
- Smola, A.J.; Schölkopf, B. A Tutorial on Support Vector Regression. Stat. Comput. 2004, 14, 199–222. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 785–794. [Google Scholar] [CrossRef]
- Hochreiter, S.; Schmidhuber, J. Long Short-Term Memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef]
- Sill, J.; Takacs, G.; Mackey, L.; Lin, D. Feature-Weighted Linear Stacking. arXiv 2009, arXiv:0911.0460. [Google Scholar]
- Homepage–U.S. Energy Information Administration (EIA). Available online: https://www.eia.gov/ (accessed on 20 June 2022).
- New York Independent System Operator (NYISO). Available online: https://www3.dps.ny.gov/W/PSCWeb.nsf/All/298372E2CE4764E885257687006F39DF (accessed on 20 June 2022).
- BARON. Available online: https://www.gams.com/latest/docs/S_BARON.html (accessed on 20 June 2022).
Predictions | Load Demand (MASE) | PV Power (MASE) | LBMP (MASE) | ||||
---|---|---|---|---|---|---|---|
Forecast Model | |||||||
Level 1 | Training | Validation | Training | Validation | Training | Validation | |
LR | 1.0126 | 0.9467 | 0.8046 | 0.7526 | 0.9981 | 0.9043 | |
LogR | 1.0318 | 0.9474 | 0.7193 | 0.6930 | 0.9604 | 0.8733 | |
FNN | 0.9563 | 0.9062 | 0.4474 | 0.4953 | 0.9328 | 0.8504 | |
RNN | 1.0154 | 0.9357 | 0.4803 | 0.5000 | 0.9060 | 0.8639 | |
GPR | 0.9710 | 0.8965 | 0.4531 | 0.4882 | 0.9975 | 0.9042 | |
SVM | 0.9771 | 0.9077 | 0.4839 | 0.5060 | 0.8190 | 0.8049 | |
RF | 0.9570 | 0.8981 | 0.4352 | 0.5013 | 0.9241 | 0.8186 | |
Level 2 | |||||||
LSTM | 0.9350 | 0.8819 | 0.4223 | 0.4790 | 0.9192 | 0.8243 | |
XGBoost | 0.9406 | 0.8722 | 0.4169 | 0.4860 | 0.8793 | 0.8098 | |
Level 3 | |||||||
Blending | 0.9224 | 0.8664 | 0.4111 | 0.4778 | 0.8180 | 0.7985 |
Predictions | Load Demand (MASE) | PV Power (MASE) | LBMP (MASE) | ||||
---|---|---|---|---|---|---|---|
Forecast Model | |||||||
Level 1 | Training | Validation | Training | Validation | Training | Validation | |
LR | 0.8392 | 0.8557 | 1.0770 | 1.0780 | 0.7788 | 0.7127 | |
LogR | 0.9003 | 0.9094 | 0.6382 | 0.7976 | 0.7766 | 0.6980 | |
FNN | 0.7123 | 0.7195 | 0.6159 | 0.7613 | 0.8938 | 0.7055 | |
RNN | 0.7211 | 0.6920 | 0.6380 | 0.7691 | 0.7774 | 0.6988 | |
GPR | 0.8304 | 0.7563 | 0.6182 | 0.7692 | 0.7856 | 0.761 | |
SVM | 0.7655 | 0.6919 | 0.6504 | 0.7442 | 0.6618 | 0.6673 | |
RF | 0.7642 | 0.7649 | 0.5474 | 0.6445 | 0.7946 | 0.6866 | |
Level 2 | |||||||
LSTM | 0.4538 | 0.5201 | 0.6786 | 0.6348 | 0.6866 | 0.6690 | |
XGBoost | 0.9150 | 0.7958 | 0.5436 | 0.6107 | 0.7846 | 0.6970 | |
Level 3 | |||||||
Blending | 0.4081 | 0.5012 | 0.5157 | 0.6012 | 0.6515 | 0.6558 |
EEDOA | Economic Dispatch Optimization | Environmental Dispatch Optimization | |||
---|---|---|---|---|---|
Evaluation | Forecast | No Forecast | Forecast | No Forecast | |
Grid-connected mode | |||||
CT $/mon | 18,589.21 | 19,061.08 | 21,578.47 | 21,956.97 | |
ET kg/mon | 404,914.61 | 412,465.08 | 403,682.54 | 412,028.02 | |
Islanded mode | |||||
CT $/mon | 74,991.03 | 79,087.03 | 78,639.83 | 83,427.78 | |
ET kg/mon | 416,585.41 | 437,626.21 | 413,979.07 | 433,580.64 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wu, W.; Chou, S.-C.; Viswanathan, K. Optimal Dispatching of Smart Hybrid Energy Systems for Addressing a Low-Carbon Community. Energies 2023, 16, 3698. https://doi.org/10.3390/en16093698
Wu W, Chou S-C, Viswanathan K. Optimal Dispatching of Smart Hybrid Energy Systems for Addressing a Low-Carbon Community. Energies. 2023; 16(9):3698. https://doi.org/10.3390/en16093698
Chicago/Turabian StyleWu, Wei, Shih-Chieh Chou, and Karthickeyan Viswanathan. 2023. "Optimal Dispatching of Smart Hybrid Energy Systems for Addressing a Low-Carbon Community" Energies 16, no. 9: 3698. https://doi.org/10.3390/en16093698