Huajie Ding

Rolling Optimization of Wind Farm and Energy Storage System in Electricity Markets

Intraday energy markets have been established in some power markets mainly because of large-scale wind power integration. Inspired by the Spanish power market, this paper proposes a modified market design which contains day-ahead and intraday energy bidding sections to better accommodate stochastic wind energy. Then coordinated operation of the wind farm (WF) and energy storage system (ESS) is studied. Rolling stochastic optimization formulations for day-ahead, intraday biddings and real-time operations are put forward to obtain the optimal bidding strategy of WF-ESS union in each bidding section to maximize its overall profit. Case studies and sensitivity analyses are carried out on a union of WFs and a pumped storage plant (PSP). Simulation results show that the proposed rolling optimization method can effectively utilize the updated wind power forecast data and regulation capability of ESS, and thus increase profit for the union prominently.

Integrated Bidding and Operating Strategies for Wind-Storage Systems

Due to their flexible charging and discharging capabilities, energy storage systems (ESS) are considered a promising complement to wind farms (WFs) participating in electricity markets. This paper presents integrated day-ahead bidding and real-time operation strategies for a wind-storage system to perform arbitrage and to alleviate wind power deviations from day-ahead contracts. The strategy is developed with two-price balancing markets in mind. A mixed integer nonlinear optimization formulation is built to determine optimal offers by taking into account expected wind power forecasting errors and the power balancing capability of the ESS. A modified gradient descent algorithm is designed to solve this nonlinear problem. A number of case studies validate the computational efficiency and optimality of the algorithm. Compared to the existing strategies, the proposed strategies yield increased economic profit, regardless of the temporal dependence of wind power forecasting errors.

Optimal Offering and Operating Strategies for Wind-Storage Systems With Linear Decision Rules

The participation of wind farm-energy storage systems (WF-ESS) in electricity markets calls for an integrated view of day-ahead offering strategies and real-time operation policies. Such an integrated strategy is proposed here by co-optimizing offering at the day-ahead stage and operation policy to be used at the balancing stage. Linear decision rules are seen as a natural approach to model and optimize the real-time operation policy. These allow enhancing profits from balancing markets based on updated information on prices and wind power generation. Our integrated strategies for WF-ESS in electricity markets are optimized under uncertainty in both wind power and price predictions. The resulting stochastic optimization problem readily yields optimal offers and linear decision rules. By adding a risk-aversion term in form of conditional value at risk into the objective function, the optimization model additionally provides flexibility in finding a trade-off between profit maximization and risk management. Uncertainty in wind power generation, as well as day-ahead and balancing prices, takes the form of scenario sets, permitting to reformulate the optimization problem as a linear program. Case studies validate the effectiveness of the strategy proposed by highlighting and quantifying benefits w.r.t. other existing strategies.

Determination of Weight Coefficient for Power System Restoration

This letter presents a novel method to determine the weight coefficient existing in the model of network reconfiguration during power system restoration. The data envelopment analysis (DEA) method incorporating preference information is employed to implement the modeling and evaluation of the weight coefficient with respect to each transmission line. Some practical factors involving line charging reactive power, weather condition, equipment reliability, line operation time, and transmission capability are taken into account during analysis.

Coordinated operational strategy of energy storage system and wind farm

This paper proposes a coordinated operational strategy of energy storage system (ESS) and wind farm to reduce wind power spillage and alleviate wind power fluctuation. Firstly, power curve of a wind farm, in the form of an equivalent wind turbine, is determined by least square fitting. Based on the power output characteristic of the wind farm and historic operating data, the statistical properties of wind power spillage and fluctuation can be obtained. Secondly, the target for wind farm and ESS power output is calculated. Power generation/absorption capabilities of ESS and available power output reducing range of the wind farm are quantified as well, considering the operational constraints of ESS and dispatched wind power output. Finally, a coordinated operational strategy is developed not only to adjust the combined ESS and wind farm to meet the dispatch requirement, but also to ensure ESS and the wind farm to satisfy operational constraints. Case studies are conducted on a practical wind farm with battery energy storage system installed, which demonstrate that the coordination of ESS can greatly boost the revenue and improve power quality of wind farm.

Optimal Offering and Operating Strategy for a Large Wind-Storage System as a Price Maker

Wind farms and energy storage systems are playing increasingly more important roles in power systems, which makes their offering nonnegligible in some markets. From the perspective of wind farm-energy storage systems (WF-ESS), this paper proposes an integrated strategy of day-ahead offering and real-time operation policies to maximize their overall profit. As participants with large capacity in electricity markets can influence cleared prices by strategic offering, a large scaled WF-ESS is assumed to be a price maker in day-ahead markets. Correspondingly, the strategy considers influence of offering quantity on cleared day-ahead prices, and adopts linear decision rules as the real-time control strategy. These allow enhancing overall profits from both day-ahead and balancing markets. The integrated price-maker strategy is formulated as a stochastic programming problem, where uncertainty of wind power generation and balancing prices are taken into account in the form of scenario sets, permitting to reformulate the optimization problem as a linear program. Case studies validate the effectiveness of the proposed strategy by highlighting and quantifying benefits comparing with the price-taker strategy, and also show the profit enhancement brought to the distributed resources.

Improving offering strategies for wind farms enhanced with storage capability

Due to the flexible charging and discharging capability, energy storage system (ESS) is thought of as a promising complement to wind farms (WF) in participating into electricity markets. This paper proposes a reserve-based real-time operation strategy of ESS to make arbitrage and to alleviate the wind power deviation from day-ahead contracts. Taking into account the operation strategy as well as two-price balancing market rules, a day-ahead bidding strategy of WF-ESS system is put forward and formulated. A modified gradient descent algorithm is described to solve the formulations. In the case studies, the computational efficiency of the algorithm is validated firstly. Moreover, a number of scenarios with/without considering the temporal dependence of wind power forecast error are designed and employed to compare the proposed strategy with other common ones in terms of profit.

Optimal intra-day coordination of wind farm and pumped-hydro-storage plant

Intra-day markets have been set up in some power markets with large scale integration of wind power generation. Inspired by Spanish market, this paper proposes a modified intra-day bidding mechanism to better accommodate stochastic wind energy. Then the coordinated operation of wind farm (WF) and pumped-hydro-storage plant (PHSP) is studied. Firstly, wind power forecast errors are classified according to forecast wind speed. The corresponding error distribution is represented by a versatile probability function, which is explicitly reversible. Secondly, a chance-constrained optimization formulation is proposed to obtain the optimal bidding strategy of WF and PHSP. Case studies and sensitivity analyses are carried out. Simulation results demonstrate that the intra-day bidding mechanism can effectively utilize the updated wind power forecast data, and the proposed optimization method can increase profit for the union of WF and PHSP.

Coordinated control strategy of energy storage system with electric vehicle charging station

Energy storage system (ESS) is regarded as a promising supplement for electric vehicle (EV) fast charging station. This paper works on the coordinated operation of EV fast charging stations with ESS. Firstly, considering characteristics of EV fast charging load, charging and discharging periods of ESS within a day are optimized based on historical average charging load. Secondly, a real-time control strategy is proposed not only to follow and smooth the charging load but also to avoid electricity absorption from power grid in high-price periods. Case studies are conducted on a practical EV fast charging station with battery ESS installed. Simulation results demonstrate that the proposed control method can greatly reduce the electricity purchase cost and smooth the power demand within the charging station.