Optimal Bidding Strategy for Renewable Microgrid with Active Network Management
Abstract
:1. Introduction
2. Microgrid Operators as Market Participant
Reference | Tolerance Band | Threshold Level | Penalty Price Factor | |
---|---|---|---|---|
Over-Scheduling Imbalance | Under-Scheduling Imbalance | |||
Shi et al. [10] | None | None | 20% of MP | 15% of MP |
Nguyen et al. [11] | None | None | Peak price of 24 h | Peak price of 24 h |
CAISO [17] | ±5% of bid | >5% | 25% of MP | 25% of MP |
>10% | 50% of MP | 100% of MP |
3. Basic Bidding Strategy Without Active Network Management
4. Proposed Bidding Strategy with Active Network Management
4.1. Features of Active Network Management in Microgrid Operation
4.2. Formulation of Optimal Bidding Strategy with Active Network Management
4.2.1. Objective function
4.2.2. Load constraints
4.2.3. Generation constraints
4.2.4. BESS constraints
4.2.5. Power flow constraints
5. Simulation
5.1. Simulation Settings
Resource types | Properties | Values | ||||
---|---|---|---|---|---|---|
WT | Location | Bus 6 | Bus 12 | Bus 18 | Bus 19 | Bus 31 |
Capacity (MVA) | 1.2 | 0.6 | 0.6 | 0.96 | 1.2 | |
PV | Location | Bus 7 | Bus 9 | Bus 11 | Bus 21 | Bus 33 |
Capacity (MVA) | 0.24 | 0.36 | 0.36 | 0.36 | 0.6 | |
BESS | Location | Bus 2 | Bus 10 | Bus 13 | Bus 20 | Bus 30 |
Max. Power (MVA) | 0.18 | 0.06 | 0.06 | 0.18 | 0.30 | |
Capacity (MWh) | 0.36 | 0.12 | 0.12 | 0.36 | 0.60 |
Simulation Case | Tolerance Band | Penalty Price Factor | |
---|---|---|---|
Over-Scheduling | Under-Scheduling | ||
Case I | ±5% | 50% | 50% |
Case II | 75% | 75% |
Generation curtailment compensation factor γGCURT | 80% |
Load curtailment compensation factor γLCURT | 300% |
Fast charging/cycling cost parameters αB, βB | 1/0.75 |
Minimum/maximum state-of-charge of the BESS | 20%/95% |
∆t | 1 h |
Predetermined power factor range of RDGs | 0.9 Lagging–0.9 Leading |
Predetermined power factor range of the line connecting the main grid and MG | 0.95 Lagging–0.95 Leading |
Lower/upper voltage limit VLL, VUL | 0.95 p.u./1.05 p.u. |
5.2. Simulation Results
Simulation Case | Case I | Case II | |||||
---|---|---|---|---|---|---|---|
Basic | Proposed | Difference | Basic | Proposed | Difference | ||
Revenue | 6484.14 | 6483.90 | −0.24 | 6483.62 | 6482.64 | −0.98 | |
Costs | Exchange cost | 1562.30 | 1562.90 | 0.60 | 1563.24 | 1571.00 | 7.77 |
Imbalance cost | 54.04 | 13.10 | −40.94 | 78.44 | 9.55 | −68.90 | |
Loss cost | 66.55 | 65.68 | −0.86 | 67.26 | 64.91 | −2.35 | |
Battery wear cost | 0.18 | 0.18 | 0.00 | 0.20 | 0.24 | 0.04 | |
Generation cost | 1728.98 | 1727.69 | −1.28 | 1729.61 | 1719.88 | −9.73 | |
Generation curtailment cost | 110.67 | 111.69 | 1.03 | 110.16 | 117.95 | 7.78 | |
Load curtailment cost | 221.68 | 221.89 | 0.21 | 222.98 | 223.91 | 0.93 | |
Profit | 2739.75 | 2780.76 | 41.01 | 2711.73 | 2775.20 | 63.48 |
6. Conclusions
Author Contributions
Conflicts of Interest
Nomenclatures
- Abbreviations
MG Microgrid MGO Microgrid Operator SO System Operator of the main grid RDG Renewable Distributed Generation WT Wind Turbine PV Photovoltaic system BESS Battery Energy Storage System ANM Active network management MP Market Price in day-ahead energy market TOU Time-of-Use tariff - Indices and Sets
s Index of each bus in the MG i Index of each bus in the MG t Index of each time stage l Index of each distribution line T Set of time stages B Set of buses in the MG - Functions
PRs Daily operating profit for scenario s Rs Daily operating revenue for scenario s Daily load curtailment cost for scenario s Daily generation cost for scenario s Daily generation curtailment cost for scenario s Daily loss cost for scenario s Daily exchange cost in the market for scenario s Daily imbalance cost for scenario s Daily battery wear cost for scenario s Total loss in the MG at time stage t for scenario s - Variables
, Dispatched active/reactive loads in the i-th bus at time stage t for scenario s , Dispatched active/reactive power outputs of the RDG in the i-th bus at time stage t for scenario s , Scheduled active/reactive powers of the BESS at the i-th bus at time stage t for scenario s State-of-charge of the BESS in the i-th bus at time stage t for scenario s , Active/reactive power flows in the line connecting the main grid and MG at time stage t for scenario s , Active/reactive power flows in the i-th distribution line at time stage t for scenario s , Magnitude/angle of the voltage of the i-th bus at time stage t for scenario s Submitted bid in the day-ahead energy market at time stage t - Parameters
, Forecasted active/reactive power demands in the i-th bus at time stage t for scenario s Forecasted active power output of the RDG in the i-th bus at time stage t for scenario s Capacity of the RDG in the i-th bus , Minimum/maximum state-of-charge of the BESS in the i-th bus at time stage t Maximum power output of the BESS in the i-th bus , Capacities of the i-th distribution line and the line connecting the main grid and MG Probability of scenario s Retail price at time stage t Day-ahead market price at time stage t , Penalty price factors for the under/over-scheduling imbalance , Load and generation curtailment compensation factors b Half-width of the tolerance band , Fast charging and cycling cost parameters of the BESS Duration of each time stage , Lower/upper voltage limits , Conductance/susceptance of the distribution line between the i-th bus and the j-th bus , Limit of the power factor for RDG and the power flow on the connecting line
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Kim, S.W.; Kim, J.; Jin, Y.G.; Yoon, Y.T. Optimal Bidding Strategy for Renewable Microgrid with Active Network Management. Energies 2016, 9, 48. https://doi.org/10.3390/en9010048
Kim SW, Kim J, Jin YG, Yoon YT. Optimal Bidding Strategy for Renewable Microgrid with Active Network Management. Energies. 2016; 9(1):48. https://doi.org/10.3390/en9010048
Chicago/Turabian StyleKim, Seung Wan, Jip Kim, Young Gyu Jin, and Yong Tae Yoon. 2016. "Optimal Bidding Strategy for Renewable Microgrid with Active Network Management" Energies 9, no. 1: 48. https://doi.org/10.3390/en9010048
APA StyleKim, S. W., Kim, J., Jin, Y. G., & Yoon, Y. T. (2016). Optimal Bidding Strategy for Renewable Microgrid with Active Network Management. Energies, 9(1), 48. https://doi.org/10.3390/en9010048