Intelligent Demand Side Management for Exhaustive TechnoEconomic Analysis of Microgrid System
Abstract
:1. Introduction
1.1. Related Work
1.2. Motivation and Contribution
1.3. Arrangement of the Proposed Paper
2. Mathematics of Fitness Function and its Formulation
2.1. Strategy Incorporated in DSM
2.2. Procedure to Attain a Restructured Load Model by Means of DSM Approach
2.3. Differential Evolution Algorithm
3. Descriptive TechnoEconomic Analysis of a Subject Test System
3.1. Description of the Subject Test System and Simulation Environment
3.1.1. Stage 1: Effects of DSM Implementation
3.1.2. Stage 2: Exhaustive TechnoEconomic Analysis
Case 1  Without DERs: it is assumed that the grid supplies the entire load demand of the MG system and there is no DER, neither RES nor fossilfueled DERs are present. In that case, the generation cost of the MG system per day was $23,203 for forecasted load demand, which reduced gradually to $22,095 when DSM participation was increased up to 40%. 
Case 2  Ideal Case: As hinted by the name of the scenario, this is the ideal case of MG operation, meaning all the DERs are actively participating to share the load demands every hour. In this case, the grid buys and sells power to and from the MG at the same price (denoted by CP in Figure 6). The generation cost in this case was found to be $8326, which is a 64% reduction from the previous case. This signifies the benefits of an MG system where DERs share the load demand, instead of being solely dependent on the grid. Moreover, the generation cost dropped to $7311 when the restructured load demand with 40% DSM participation level was considered. Figure 8 displays the load sharing model of the DERs for this case when generation cost was minimized to $7311. The grid actively buying and selling power can be clearly seen from Figure 6. Since this is considered to be the ideal case, the rest of the cases shall be compared with this. 
Case 3  Without RES: The contribution of the WT was not considered in this scenario. The total output of the WT as shown in Figure 5 was 5525 kW, which is 7% of the total load demand of the MG system. The generation cost in this case was found to be $9857, which is an approximate 18% increase over the ideal case. 
Case 4  Passive Grid: In this case, the participation of the grid is demandcentric. This means that the grid will only supply power if the load demand is not being met by the rest of the DERs. For the rest of the time, the grid will be in standby mode. Mathematically, this scenario can be achieved by fixing the lower limit of the grid to zero. Since the grid is not actively participating in buying and selling the power, it is quite obvious that the generation cost in this case ($8571) will be more than the ideal case ($8326). Figure 9 shows the hourly output of DERs when the generation cost was minimized for Case 4 with 40% DSM. It can be seen that the grid is supplying power from hours 1 through 7 and 23. There is no negative value of the grid in the figure, unlike Figure 8. 
Case 5 & Case 6  Taxable SP: The grid buys and sells power with different amounts and the same was mathematically modelled in (3) and (4) above. The tax was 20% for Case 5 and 50% for Case 6. The generation cost with 20% tax was found to be $8475 and with 50% tax was found to be $8562. In either of the cases, the generation cost was found to be more than the ideal case both with and without DSM. 
Case 7  Different SP/CP: The electricity market price in this case is followed as shown in Figure 6. Here, the generation cost is more than the ideal case too. 
Overall observation 

4. Conclusions
 The generation cost is minimized when the grid actively buys and sells power to and from the MG system. Passive participation of the grid may act as backup to supply deficit power but have no role in decreasing the MG cost.
 TOUbased electricity market pricing strategy where the grid buys and sells power with the same price incurs the least generation cost compared to any other electricity market pricing strategies.
 DSM plays a significant role in minimizing the generation cost of the MG system. Additionally, it also improves the load factor of the system and reduces the peak demand, as observed by the results obtained.
5. Future Works
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
 Ghosh, B.; Dey, B.; Bhattacharya, A. Solving economic load dispatch problem using hybrid Krill Herd algorithm. In Proceedings of the 2015 International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), Shillong, India, 12–13 June 2015. [Google Scholar]
 Yalcinoz, T.; Short, M.J. Neural networks approach for solving economic dispatch problem with transmission capacity constraints. IEEE Tran. on Pow. Sys. 1998, 13, 307–313. [Google Scholar] [CrossRef]
 Dhillon, J.; Parti, S.C.; Kothari, D.P. Stochastic economic emission load dispatch. Elect. Pow. Sys. Res. 1993, 26, 179–186. [Google Scholar] [CrossRef]
 Wang, C.; Shahidehpour, S.M. Ramprate limits in unit commitment and economic dispatch incorporating rotor fatigue effect. IEEE Tran. on Pow. Sys 1994, 9, 1539–1545. [Google Scholar] [CrossRef]
 Kai, S.; Qing, L.; Jizhen, L.; Yuguang, N.; Ruifeng, S.; Yang, B. New combination strategy of genetic and tabu algorithm an economic load dispatching case study. In Proceedings of the 2011 Chinese Control and Decision Conference (CCDC), Mianyang, China, 23–25 May 2011. [Google Scholar]
 Bhattacharya, A.; Chattopadhyay, P.K. Solving complex economic load dispatch problems using biogeographybased optimization. Exp. Sys. with App. 2010, 37, 3605–3615. [Google Scholar] [CrossRef]
 Sinha, N.; Chakrabarti, R.; Chattopadhyay, P.K. Evolutionary programming techniques for economic load dispatch. IEEE Trans. Evol. Comp. 2003, 7, 83–94. [Google Scholar] [CrossRef]
 Daniel, L.; Chaturvedi, K.T.; Kolhe, M.L. Dynamic Economic Load Dispatch using Levenberg Marquardt Algorithm. Energy Procedia 2018, 144, 95–103. [Google Scholar] [CrossRef]
 Hosseinnezhad, V.; Babaei, E. Economic load dispatch using θPSO. Int. J. Electr. Pow. Energy Syst. 2013, 49, 160–169. [Google Scholar] [CrossRef]
 Yang, X.; Leng, Z.; Xu, S.; Yang, C.; Yang, L.; Liu, K.; Zhang, L. Multiobjective optimal scheduling for CCHP microgrids considering peakload reduction by augmented εconstraint method. Renew. Energy 2021, 172, 408–423. [Google Scholar] [CrossRef]
 Nwulu, N.I.; Xia, X. Optimal dispatch for a microgrid incorporating renewables and demand response. Renew. Energy 2017, 101, 16–28. [Google Scholar] [CrossRef]
 Coelho, V.N.; Coelho, I.M.; Coelho, B.N.; Cohen, M.W.; Reis, A.J.; Silva, S.M.; Souza, M.J.; Fleming, P.J.; Guimarães, F.G. Multiobjective energy storage power dispatching using plugin vehicles in a smartmicrogrid. Renew. Energy 2016, 89, 730–742. [Google Scholar] [CrossRef]
 Grefenstette, J.J. Genetic algorithms and machine learning. In Proceedings of the Sixth Annual Conference on Computational Learning Theory, Santa Cruz, CA, USA, 26–28 July 1993. [Google Scholar]
 Dutta, S.; Reddy, M.J.B.; Mohanta, D.K.; Kushwah, M.S.; Sadhu, P.K. μPMUbased intelligent island detection–the first crucial step toward enhancing grid resilience with MG. IET Sma. Gri. 2020, 3, 162–173. [Google Scholar] [CrossRef]
 Chen, C.; Duan, S.; Cai, T.; Liu, B.; Hu, G. Smart energy management system for optimal microgrid economic operation. IET Renew. Power Gener. 2011, 5, 258–267. [Google Scholar] [CrossRef]
 Kasaei, M.J. Energy and operational management of virtual power plant using imperialist competitive algorithm. Int. Trans. Electr. Energy Syst. 2018, 28, e2617. [Google Scholar] [CrossRef]
 Basu, M.A.; Chowdhury, A. Cuckoo search algorithm for economic dispatch. Energy 2013, 60, 99–108. [Google Scholar] [CrossRef]
 Moghaddam, A.A.; Seifi, A.; Niknam, T.; Pahlavani, M.R.A. Multiobjective operation management of a renewable MG (microgrid) with backup microturbine/fuel cell/battery hybrid power source. Energy 2011, 36, 6490–6507. [Google Scholar] [CrossRef]
 Rabiee, A.; Sadeghi, M.; Aghaei, J. Modified imperialist competitive algorithm for environmental constrained energy management of microgrids. J. Clean. Prod. 2018, 202, 273–292. [Google Scholar] [CrossRef]
 Trivedi, I.N.; Jangir, P.; Bhoye, M.; Jangir, N. An economic load dispatch and multiple environmental dispatch problem solution with microgrids using interior search algorithm. Neur. Comp. App. 2018, 30, 2173–2189. [Google Scholar] [CrossRef]
 Elattar, E.E. Modified harmony search algorithm for combined economic emission dispatch of microgrid incorporating renewable sources. Energy 2018, 159, 496–507. [Google Scholar] [CrossRef]
 Faseela, C.K.; Vennila, H. Economic and emission dispatch using Whale Optimization Algorithm (WOA). Int. J. Electr. Comput. Eng. 2018, 8, 1297. [Google Scholar] [CrossRef]
 Kumar, K.P.; Saravanan, B.; Swarup, K.S. Optimization of Renewable Energy Sources in a Microgrid Using Artificial Fish Swarm Algorithm. Energy Procedia 2016, 90, 107–113. [Google Scholar] [CrossRef]
 Gholami, K.; Dehnavi, E. A modified particle swarm optimization algorithm for scheduling renewable generation in a microgrid under load uncertainty. Appl. Soft Comp. 2019, 78, 496–514. [Google Scholar] [CrossRef]
 Askarzadeh, A. A MemoryBased Genetic Algorithm for Optimization of Power Generation in a Microgrid. IEEE Trans. Sustain. Energy 2017, 9, 1081–1089. [Google Scholar] [CrossRef]
 Ramli, M.A.; Bouchekara, H.R.E.H.; Alghamdi, A.S. Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources. Sustainability 2019, 11, 3839. [Google Scholar] [CrossRef] [Green Version]
 Maulik, A.; Das, D. Optimal operation of microgrid using four different optimization techniques. Sustain. Energy Technol. Assessments 2017, 21, 100–120. [Google Scholar] [CrossRef]
 Nayak, A.; Maulik, A.; Das, D. An integrated optimal operating strategy for a gridconnected AC microgrid under load and renewable generation uncertainty considering demand response. Sustain. Energy Technol. Assess. 2021, 45, 101169. [Google Scholar] [CrossRef]
 Harsh, P.; Das, D. Energy management in microgrid using incentivebased demand response and reconfigured network considering uncertainties in renewable energy sources. Sustain. Energy Technol. Assess. 2021, 46, 101225. [Google Scholar] [CrossRef]
 Tah, A.; Das, D. Operation of small hybrid autonomous power generation system in isolated, interconnected and grid connected modes. Sustain. Energy Technol. Assess. 2016, 17, 11–25. [Google Scholar] [CrossRef]
 Younes, Z.; Alhamrouni, I.; Mekhilef, S.; Reyasudin, M. A memorybased gravitational search algorithm for solving economic dispatch problem in microgrid. Ain Shams Eng. J. 2021, 12, 1985–1994. [Google Scholar] [CrossRef]
 Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey wolf optimizer. Adv. Eng. Soft. 2014, 69, 46–61. [Google Scholar] [CrossRef] [Green Version]
 Khandelwal, A.; Bhargava, A.; Sharma, A.; Sharma, H. Modified Grey Wolf Optimization Algorithm for Transmission Network Expansion Planning Problem. Arab. J. Sci. Eng. 2018, 43, 2899–2908. [Google Scholar] [CrossRef]
 Mirjalili, S. SCA: A Sine Cosine Algorithm for solving optimization problems. Knowl. Based Syst. 2016, 96, 120–133. [Google Scholar] [CrossRef]
 Askarzadeh, A. A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput. Struct. 2016, 169, 1–12. [Google Scholar] [CrossRef]
 Das, S.; Suganthan, P.N. Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems; Jadavpur University, Nanyang Technological University: Kolkata, India, 2010. [Google Scholar]
 RizkAllah, R.M.; Hassanien, A.E.; Bhattacharyya, S. Chaotic crow search algorithm for fractional optimization problems. Appl. Soft Comput. 2018, 71, 1161–1175. [Google Scholar] [CrossRef]
 Kumar, K.P.; Saravanan, B. Day ahead scheduling of generation and storage in a microgrid considering demand Side management. J. Energy Storage 2019, 21, 78–86. [Google Scholar] [CrossRef]
 Sharma, S.; Bhattacharjee, S.; Bhattacharya, A. Operation cost minimization of a MicroGrid using QuasiOppositional Swine Influenza Model Based Optimization with Quarantine. Ain Shams Eng. J. 2018, 9, 45–63. [Google Scholar] [CrossRef] [Green Version]
 Sharma, S.; Bhattacharjee, S.; Bhattacharya, A. Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid. IET Gener. Transm. Distrib. 2016, 10, 625–637. [Google Scholar] [CrossRef]
 Wei, J.; Zhang, Y.; Wang, J.; Wu, L.; Zhao, P.; Jiang, Z. Decentralized Demand Management Based on Alternating Direction Method of Multipliers Algorithm for Industrial Park with CHP Units and Thermal Storage. J. Mod. Power Syst. Clean Energy 2022, 10, 120–130. [Google Scholar] [CrossRef]
 Dey, B.; Raj, S.; Mahapatra, S.; Márquez, F.P.G. Optimal scheduling of distributed energy resources in microgrid systems based on electricity market pricing strategies by a novel hybrid optimization technique. Int. J. Electr. Power Energy Syst. 2021, 134, 107419. [Google Scholar] [CrossRef]
 Dey, B.; Bhattacharyya, B.; Devarapalli, R. A novel hybrid algorithm for solving emerging electricity market pricing problem of microgrid. Int. J. Intell. Syst. 2021, 36, 919–961. [Google Scholar] [CrossRef]
 Lokeshgupta, B.; Sivasubramani, S. Multiobjective harmony search algorithm for dynamic optimal power flow with demand side management. Electr. Power Comp. Syst. 2019, 47, 692–702. [Google Scholar]
 Lokeshgupta, B.; Sivasubramani, S. Multiobjective dynamic economic and emission dispatch with demand side management. Int. J. Electr. Power Energy Syst. 2018, 97, 334–343. [Google Scholar] [CrossRef]
 Datta, J.; Das, D. Stochastic Energy Management of gridconnected microgrid considering battery degradation cost and renewables penetration. In Proceedings of the 2020 IEEE International Conference on Power Systems Technology, Bangalore, India, 14–16 September 2020. [Google Scholar]
Distributed Generation Units  Micro Turbine  Combined Heat and Power  Natural Gas Fuel Cell  Biomass  Grid 

P_{min} (kW)  100  100  100  100  −800 
P_{max} (kW)  600  800  800  1000  800 
Fuel Cost ($/kWh)  0.021  0.027  0.027  0.063  Figure 6 
Operational and Maintenance Cost ($/kWh)  0.00587  0.00419  0.00419  0.01258  NA 
Efficiency  0.3  0.35  0.35  0.29  NA 
Emission (kg/kWh)  0.724  0.408  0.336  0.003  0.547 
Case  Brief Nomenclature  Detailed Description 

1  Without DER  Only grid will supply power and fulfill demand of the MG 
2  Ideal Case  All the DERs and grid will participate actively to share demand. Grid will buy and sell electricity with the same price. 
3  Without RES  Same as scenario 2 excluding the contribution wind system 
4  Passive Grid  Grid acts as back up to supply (sell) power only [42]. 
5  20% taxable price  Cost function of grid follows (3) and (4); tax = 20% [42,46] 
6  50% taxable price  Cost function of grid follows (3) and (4); tax = 50% [42,46] 
7  Different CP/SP  Electricity price followed as Figure 6 [42]. 
Without DSM  10% DSM  20% DSM  30% DSM  40% DSM  

Total Demand (kW)  73,928.5  73,928.4993  73,928.4996  73,928.5001  73,928.5 
Peak Demand (kW)  3715  3639.0785  3539.7408  3452.602  3404.255 
Average Demand (kW)  3080.3541  3080.3541  3080.3541  3080.356  3080.354 
Peak Reduction (%)  Ref  2.04  4.73  7.07  8.371 
Load Factor  0.8291  0.8464  0.8702  0.8921  0.9048 
Case 1  Case 2  Case 3  Case 4  Case 5  Case 6  Case 7  

Without DSM  23,203  8326  9857  8571  8475  8562  8475 
10% DSM  22,924  8047  9578  8264  8179  8257  8179 
20% DSM  22,646  7769  9299  8000  7908  7996  7908 
30% DSM  22,405  7528  9059  7771  7679  7771  7679 
40% DSM  22,095  7311  8781  7744  7513  7690  7513 
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Dey, B.; Dutta, S.; Garcia Marquez, F.P. Intelligent Demand Side Management for Exhaustive TechnoEconomic Analysis of Microgrid System. Sustainability 2023, 15, 1795. https://doi.org/10.3390/su15031795
Dey B, Dutta S, Garcia Marquez FP. Intelligent Demand Side Management for Exhaustive TechnoEconomic Analysis of Microgrid System. Sustainability. 2023; 15(3):1795. https://doi.org/10.3390/su15031795
Chicago/Turabian StyleDey, Bishwajit, Soham Dutta, and Fausto Pedro Garcia Marquez. 2023. "Intelligent Demand Side Management for Exhaustive TechnoEconomic Analysis of Microgrid System" Sustainability 15, no. 3: 1795. https://doi.org/10.3390/su15031795