Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs †
Abstract
:1. Introduction
- (1)
- The technical constraints of the power system need to be considered. EVs considerably increase the electricity demand and have significant influences on the power system. The adverse effects of high penetration level of EVs on the power grid when they charge themselves in the grid to vehicle (G2V) mode are briefly reviewed in [3]. However, in the vehicle to grid (V2G) mode, active and reactive power injected to the network by EVs, if controlled well, can decrease the peak power [12] and provide ancillary services [13] instead of causing adverse effects. Therefore, although most of existing DSM programs neglect the technical constraints of the power system to simplify mathematical calculations [11], considering the technical constraints is critical for practical deployment of future power systems.
- (2)
- Individual benefit of each customer needs to be taken into account. If whole network welfare is selected as a goal function, customers may be discouraged from participating in the DSM program. A multi-agent framework is proposed in [14] to minimize the electricity bill of each household while considering the piecewise linear function for each customer’s cost. Still, this method neglects the influence of customers’ decision on each other and so it cannot prevent rebounding peaks.
- (3)
- Indirect control methods are needed to give decision authorities to the customers. Generally, DSM programs can be categorized into two types based on the control method used: direct and indirect. In direct control methods, elastic loads, including EVs, are directly controlled by a central entity of aggregators or retailers in their region. On the other hand, in indirect control methods, customers make their own decisions on elastic loads. Here, the job of the retailers is to indirectly lead customers to the desirable optimum by changing the price electricity or giving incentives to them. Since direct methods can relatively better handle the uncertainties associated with charging of EVs, e.g., departure or arrival times, most of the researchers prefer direct methods. For example, the demand peak is minimized by directly controlling EVs in [15], the cost of the whole network is decreased using a day-ahead pricing model in the presence of EVs in [16], and the direct charging and discharging method is developed to sell spinning reserve in the wholesale market in [17]. However, the common problem of direct methods is that they take away the decision authority from customers, which may decrease the popularity and security of DSM programs [18]. Therefore, indirect methods are more promising as they are more likely to lead to consumer’s acceptance than the direct methods [19]. An indirect scheduling algorithm for EVs having an experimental demonstration at the National Technical University of Athens is presented in [20]. Although in indirect methods implementing, considering the effects of other customers’ decision is crucial. For instance, in a real system, since all customers want to optimize their own cost, they may make similar decisions, simultaneously and/or collectively, to produce a major impact on the power system known as an avalanche effect [19] or a rebound peak [8,9].
- (4)
- Imperfect competition model needs to be used to more accurately model the effects of independent customers. There are two models for competitive markets: perfect and imperfect. The perfect competition model assumes that the market price is not dependent on the decision of each participant. This model is relatively simple and many researchers adopt this model. However, an independent price may lead to rebound peaks. On the other hand, an imperfect competition or oligopoly model can be used to consider the effects of all customers’ decisions. In particular, Cournot competition model, which is an oligopoly model used to describe a market with multiple players competing for production, can be used to independently maximize their profit given their competitors’ decisions [21]. However, since using this model on a system having many participants increases the complexity of the problem, some simplification or approximation is necessary to have a feasible problem. In this direction, the authors of [22] used Cournot competition to model a dynamic price for an intelligent building without EVs and made some simplifying assumptions, such as linear inverse demand curve or having exclusive energy storage device, to solve their problem. This however limits the practicability of their approach.
- Solving the first challenge: Technical constraints of the power system are considered to prevent rebound peaks.
- Solving the second challenge: Customer participation is encouraged by minimizing the cost of each customer instead of the whole network.
- Solving the third challenge: Use of indirect method gives decision authorities to customers.
- Solving the fourth challenge: Cournot imperfect competition model takes into account the effect of decisions made by individual customers.
- Solving the implementation difficulties: The proposed heuristic two-stage iterative method solves the non-linear optimization problem quick enough for real-time operations.
2. Problem Formulation
2.1. Power System Model
2.2. Electrical Vehicle Model
2.3. Elastic Appliances Model
2.4. Transactive DSM Model
3. The Heuristic Two-Stage Iterative Method
3.1. First Stage: Customer Side
3.2. Scheduling Start Time of Elastic Appliances
Algorithm 1. First Stage—Part 1: Schedule Elastic Appliances |
1: Order the i-th customer’s elastic appliances from the smallest tend,ik − td,ik (k = 1) to the largest tend,ik − td,ik (k = ne,i). |
2: For k = 1 to ne,i do |
3: Calculate for |
4: Check the constraint (5) for all |
5: Select the lowest cost that satisfies (5). |
3.3. Scheduling Charging and Discharging of EVs
Algorithm 2. First Stage—Part 2: Schedule EVs |
1: Calculate Et,i from (15). |
2: Order the time interval (from the lowest price to the highest price). |
3: While Et,i > 0 do (sequentially from the list of line 2) |
4: Calculate PEV,i(t) using (16). |
5: Update Et,i and pmax,ch,i(t) using (17) and (18). |
6: Calculate the SOC for all intervals from (7). |
7: Order all possible combinations of (th − tl) (in decreasing order). |
8: While ProfitV2G,ihl > 0 do (sequentially from the list created in line 7) |
9: Calculate ProfitV2G,ihl using (19) and (20). |
10: Update pEV,i, pmax,dch,i(td), and pmax,ch,i(tl) using (21)–(24). |
3.4. Second Stage: Grid Side
Algorithm 3. Second Stage—Part 1: Update New Price and Equilibrium Limits |
1: For i = 1 to k (k = the iteration count) do |
2: If πi < πi+1 then πmin = min(πmin, πi) |
3: If πi > πi+1 then πmax = max(πmax, πi) |
4: πnew = (πmin + πmax)/2 |
Algorithm 4. Second Stage—Part 2: Allocate of Residual Power |
1: Select πmax as π*. |
2: Calculate P (πmax) from Algorithm 1 and 2. |
3: Calculate P* from supply curve. |
4: Pres = P* − P (πmax) |
3: While Pres > 0 do |
4: Select an EV randomly, change its price to πmin, and calculate Pi (πmax). |
5: Pres = Pres − (Pi (πmin) − Pi (πmax)) |
4. Case Study
4.1. Simulation Setup
4.2. Simulation Result
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
References
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Case | Description | Pmax (kW) | Qmax (kVAr) | MCmax ($/kW) | Eloss/∑P (%) | Vmin (%) | Inelastic Cost ($) | Wet1 Cost ($) | Wet2 Cost ($) | EVs Cost ($) | Total Energy Cost ($) |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | FR without EVs | 941 | 517 | 0.1736 | 1.86 | 95.23 | 5019 | 720 | 677 | - | 6416 |
2 | FR with EVs | 2682 | 604 | 1.2979 | 3.57 | 88.38 | 15,496 | 3871 | 677 | 25,370 | 45,414 |
3 | TOU without EVs | 886 | 517 | 0.1567 | 1.63 | 95.53 | 4301 | 298 | 677 | - | 5275 |
4 | TOU with EVs | 4650 | 635 | 3.9418 | 3.74 | 80.64 | 7340 | 5484 | 677 | 54,465 | 67,966 |
5 | Proposed RTP without EVs | 767 | 378 | 0.1239 | 1.55 | 96.22 | 4160 | 269 | 486 | - | 4915 |
6 | Proposed RTP with EVs | 860 | 379 | 0.1481 | 2.03 | 95.82 | 6159 | 808 | 499 | 4849 | 12,314 |
7 | Proposed RTP with EVs in V2G mode (rb = 5 ¢/kW) | 861 | 379 | 0.1473 | 2.03 | 95.82 | 6150 | 807 | 499 | 4848 | 12,304 |
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Astero, P.; Choi, B.J.; Liang, H.; Söder, L. Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs. Energies 2017, 10, 1640. https://doi.org/10.3390/en10101640
Astero P, Choi BJ, Liang H, Söder L. Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs. Energies. 2017; 10(10):1640. https://doi.org/10.3390/en10101640
Chicago/Turabian StyleAstero, Poria, Bong Jun Choi, Hao Liang, and Lennart Söder. 2017. "Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs" Energies 10, no. 10: 1640. https://doi.org/10.3390/en10101640