- freely available
Electronics 2019, 8(8), 898; https://doi.org/10.3390/electronics8080898
- How to maximize profits for trading within a microgrid, as well as trading among multiple microgrids through pricing design?
- How to allocate energy to different priority groups in a microgrid while maximizing the profit for the sellers?
- Does considering priorities improve energy supply/demand balance at the participants in the microgrid? Can the designed pricing scheme outperform the existing pricing strategies for local energy trading?
- An optimum energy trading problem is formulated for maximizing profits within the microgrid as well as between multiple microgrids through the design of optimum pricing function. The pricing is considered as a linear function of energy sold by the houses or microgrids who have excess generation. The optimization problem for each priority group is solved in a certain stage of the solution algorithm. The optimum solutions represent the pricing signals for different priority groups and the corresponding amount of energy allocated to these groups.
- The optimum energy management solutions are evaluated through numerical simulations for intra-microgrid and inter-microgrid energy trading. The results show that the proposed approach can reduce energy mismatch at the houses after each stage in the intra-microgrid energy trading. As a result, the proposed approach has lower energy mismatch compared to the case when priorities are not considered. Moreover, the inter-microgrid energy trading can also lead to a lower energy mismatch at the microgrids.
- The optimum profits obtained by the sellers are compared with the proposed pricing and other pricing schemes, for example, flat rate, time of use (ToU) and real time pricing. The numerical results demonstrate that the proposed pricing scheme outperforms flat rate and ToU pricing, whereas its performance is similar to the real time pricing in terms of maximum profit obtained by the sellers.
2. System Model
- Priority 1: Buyers with energy demand less than 25% of their peak demand.
- Priority 2: Buyers with energy demand between 25% to 75% of their peak demand.
- Priority 3: Buyers with energy demand more than 75% of their peak demand.
- Priority 4: Buyers in who needs energy to charge their storages.
3. Optimization Problem Formulation
3.1. Intra-Microgrid Trading: Stage 1
3.2. Intra-Microgrid Trading: Stage 2
3.3. Intra-Microgrid Trading: Stage 3
3.4. Intra-Microgrid Trading: Stage 4
3.5. Inter-Microgrid Energy Trading
4. Numerical Results
4.1. Materials and Data
4.2. Energy Mismatch with Optimum Energy Trading
4.3. Profit Earned with Different Pricing Structures
4.4. Impact of Feed-in-Tariff and Energy Savings Threshold at the Buyers
4.5. Inter-Microgrid Energy Trading
Conflicts of Interest
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