Intelligent Demand Side Management for Exhaustive Techno-Economic 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 Techno-Economic 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 Techno-Economic 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 fossil-fueled 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 demand-centric. 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 stand-by 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 $8562. In either of the cases, the generation cost was found to be more than the ideal case both with and without DSM. |
Case 7 | Different SP/CP: The electricity market price in this case is followed as shown in Figure 6. Here, the generation cost is more than the ideal case too. |
Overall observation |
|
4. Conclusions
- The generation cost is minimized when the grid actively buys and sells power to and from the MG system. Passive participation of the grid may act as back-up to supply deficit power but have no role in decreasing the MG cost.
- TOU-based electricity market pricing strategy where the grid buys and sells power with the same price incurs the least generation cost compared to any other electricity market pricing strategies.
- DSM plays a significant role in minimizing the generation cost of the MG system. Additionally, it also improves the load factor of the system and reduces the peak demand, as observed by the results obtained.
5. Future Works
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Distributed Generation Units | Micro Turbine | Combined Heat and Power | Natural Gas Fuel Cell | Biomass | Grid |
---|---|---|---|---|---|
Pmin (kW) | 100 | 100 | 100 | 100 | −800 |
Pmax (kW) | 600 | 800 | 800 | 1000 | 800 |
Fuel Cost ($/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 Techno-Economic 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 Techno-Economic 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 Techno-Economic Analysis of Microgrid System" Sustainability 15, no. 3: 1795. https://doi.org/10.3390/su15031795