Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid
Abstract
:1. Introduction
- To improve the energy efficiency and ensure the stability of IMG, we propose a novel multi-microgrid system concept named “archipelago microgrid”. In our proposed system, the required power will be fully provided by the distributed power generation in local MGs. Through the energy transmission controlled by a MicroGrid Center Controller (MGCC, aggregator) among the MGs, the emission and operation cost created by local units can be mitigated. The proposed system is helpful in reducing operation cost and enhancing the stability of the grid.
- We propose a Stochastic Optimal Penetration (SOP) model, aiming to minimize the cost of the proposed “archipelago microgrid” system. To address the uncertainties from RESs and electricity prices, the optimization problem is formalized as a two-stage stochastic programming problem. In the formalized problem, the uncertain parameters such as wind, solar generation capacity are captured by the Monte Carlo-based method [8]. For the sake of comparison, the Deterministic Optimal Penetration (DOP) model based on our prior work [9] is introduced as a baseline scheme. In the optimization problem, the emission cost created by CVs and local units, the operation cost of startup/shutdown expense of units, tariff compensation, battery capacity degradation, and power losses are considered in the optimization model. The proposed stochastic model offers a desired flexibility between the environmental and economic benefits by seeking the optimal number of EVs with the considerations of the RESs and units generation limits.
- To achieve the minimization of emission and operation cost, we propose the following two schemes to schedule the optimized scale of EVs: (i) Unlimited Coordinated Scheme (UCS); and (ii) Limited Coordinated Scheme (LCS). In UCS, we consider that all the surplus energy is utilized to charge as many EVs as possible. In this ideal scenario, the mutual transmission among MGs is also allowed to avoid energy wasted. Nonetheless, peak load limits and residence preference of energy usage should be considered in practice. To this end, we propose the LCS to achieve a more realistic number of penetrated EVs to minimize the total cost.
- We carry out an extensive simulation study on a modified IEEE 9-bus system to demonstrate the effectiveness of the proposed SOP model using the two scheduling schemes. The simulation results shows that, after addressing uncertainties, the emissions and operation cost are reduced in comparison with the deterministic-based optimization model. In addition, with respect to the two schedule schemes in SOP, the LCS has been proven to be more effective on arranging the scale of EVs and realizing the emission and operation cost reduction than the UCS. Our experimental data shows that, in comparison with a baseline non-coordinated average scheme, 15.2% of emissions can be reduced and 11.2% cost can be saved in the LCS, we conduct sensitivity analysis to validate the impact of different parameters on the optimal solution.
2. System Models
2.1. Archipelago Microgrid Model
2.2. Market Model and Aggregator
3. Our Approach
3.1. Basic Idea
3.2. Problem Formulation
3.2.1. Stochastic Optimal Penetration (SOP) Model
3.2.2. Deterministic Optimal Penetration (DOP) Model
3.3. Proposed Scheduling Schemes
3.3.1. Uncoordinated Average Scheme (UAS)
3.3.2. Unlimited Coordinated Scheme (UCS)
3.3.3. Limited Coordinated Scheme (LCS)
Algorithm 1 Decentralized Algorithm | |
Require: is the number of EVs charged at time slot t in MG i | |
Ensure: , , , | |
1: | ; |
2: | Initialization: the scheduling period , the estimated vehicles number in MG i. Denote two new constraints (11) and (12) as empty; |
3: | Divide the optimization problem in Equation (1) into subproblems by scheduling intervals and queue the subproblems according to electricity prices ascending. |
4: | Set =Group of valid subproblems; |
5: | for |
6: | if then |
7: | The subproblem t is valid and add it into ; |
8: | end if |
9: | Compute the number of elements , in set of ; |
10: | for |
11: | Solve the optimization problem defined in Equation (1), subject to constraints defined in Equations (2)–(10), |
12: | if then |
13: | Add a new constraint: as constraint (11); |
14: | Recompute the optimization problem defined in Equation (1), subject to constraints (2–11); |
15: | Delete the constraints defined in Equation (11); |
16: | if then |
17: | Add new constraints: as constraint (12); |
18: | if then |
19: | break |
20: | end if |
21: | end if |
22: | end if return The optimized number of EVs . |
4. Performance Evaluation
4.1. Evaluation Methodology
4.2. Evaluation Results
4.2.1. Results of SOP and DOP
4.2.2. Sensitivity Analysis
5. Related Works
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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: | The number of local units, MGs, time slots, and scenarios |
: | The minimum battery energy stored for handling EV’s normal driving activities |
ω: | Weighting factor |
μ: | Compensation factor of price gaps |
: | Penalty factor of battery capacity degradation and power losses |
: | Charging/discharging efficiency of storage battery |
: | Fuel consumption coefficients of DG j in MG i |
: | The operation cost coefficients of DG j in MG i |
: | Cost of unit r to compensate RES prediction errors in MG i at time t |
: | The number of EVs charged/discharged at time t in MG i |
: | Minimum and real-time electricity price during the day ($/kWh) |
: | Line resistance between MG i and l (Ω) |
V: | Transmission voltage among MGs (kV) |
: | Transported power between MG i and l (kW) |
: | The number of EVs, CVs and total vehicles in MG i |
: | The power losses during power transmission (kW) |
: | Power generation of local unit j (kW) |
: | Non-EV load in MG i at time t (kW) |
: | Power generation of PV and wind (kW) |
: | Startup and shutdown cost of unit j ($) |
: | Ramp-up/down limit of unit j (kW) |
: | Charging/discharging power of the EV (kWh) |
: | Minimum/maximum power generation of unit j (kW) in MG i |
: | The maximum and minimum capacity of EV’s battery (kWh) |
: | Charging/discharging status of , where 0 indicates charging and 1 indicates discharging, respectively |
: | Operation status of unit j, where 0 means the unit’s stop status and 1 indicates its operation status, respectively |
: | Startup and shutdown status of unit j, where 0 and 1 means startup and shutdown, respectively |
SC, IC: | Slope Coefficient and Intercept Coefficient of fuel consumption per unit generation |
Rated Power (RP) (kW) | SC (a,L/h) | IC (b,L/h) |
---|---|---|
30–100 kW | 0.273 | 0.033 |
100–300 kW | 0.253 | 0.028 |
>300 kW | 0.244 | 0.014 |
MG | Type | α ($) | β ($/kWh) | (kW) | (kW) |
1 | DG | 15 | 0.13 | 20 | 200 |
2 | DG | 25 | 0.35 | 20 | 400 |
3 | DG | 40 | 0.50 | 20 | 500 |
MG | Type | ($) | ($) | (kW) | (kW) |
1 | DG | 50 | 5 | 30 | 10 |
2 | DG | 30 | 3 | 40 | 20 |
3 | DG | 20 | 2 | 50 | 30 |
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Yang, Q.; An, D.; Yu, W.; Tan, Z.; Yang, X. Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid. Sensors 2016, 16, 907. https://doi.org/10.3390/s16060907
Yang Q, An D, Yu W, Tan Z, Yang X. Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid. Sensors. 2016; 16(6):907. https://doi.org/10.3390/s16060907
Chicago/Turabian StyleYang, Qingyu, Dou An, Wei Yu, Zhengan Tan, and Xinyu Yang. 2016. "Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid" Sensors 16, no. 6: 907. https://doi.org/10.3390/s16060907
APA StyleYang, Q., An, D., Yu, W., Tan, Z., & Yang, X. (2016). Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid. Sensors, 16(6), 907. https://doi.org/10.3390/s16060907