Hrvoje Pandzic

Near-Optimal Method for Siting and Sizing of Distributed Storage in a Transmission Network

Energy storage can alleviate the problems that the uncertainty and variability associated with renewable energy sources such as wind and solar create in power systems. Besides applications such as frequency control, temporal arbitrage or the provision of reserve, where the location of storage is not particularly relevant, distributed storage could also be used to alleviate congestion in the transmission network. In such cases, the siting and sizing of this distributed storage is of crucial importance to its cost-effectiveness. This paper describes a three-stage planning procedure to identify the optimal locations and parameters of distributed storage units. In the first stage, the optimal storage locations and parameters are determined for each day of the year individually. In the second stage, a number of storage units is available at the locations that were identified as being optimal in the first stage, and their optimal energy and power ratings are determined. Finally, in the third stage, with both the locations and ratings fixed, the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion. The quality of the final solution is assessed by comparing it with the solution obtained at the first stage without constraints on storage sites or size. The approach is numerically tested on the IEEE RTS 96.

A Hybrid Stochastic/Interval Approach to Transmission-Constrained Unit Commitment

This paper proposes a new transmission-constrained unit commitment method that combines the cost-efficient but computationally demanding stochastic optimization and the expensive but tractable interval optimization techniques to manage uncertainty on the expected net load. The proposed hybrid unit commitment approach applies the stochastic formulation to the initial operating hours of the optimization horizon, during which the wind forecasts are more accurate, and then switches to the interval formulation for the remaining hours. The switching time is optimized to balance the cost of unhedged uncertainty from the stochastic unit commitment against the cost of the security premium of the interval unit commitment formulation. These hybrid, stochastic, and interval formulations are compared using Monte Carlo simulations on a modified 24-bus IEEE Reliability Test System. The results demonstrate that the proposed unit commitment formulation results in the least expensive day-ahead schedule among all formulations and can be solved in the same amount of time as a full stochastic unit commitment. However, if the range of the switching time is reduced, the hybrid formulation in the parallel computing implementation outperforms the stochastic formulation in terms of computing time.

Optimal Operation and Services Scheduling for an Electric Vehicle Battery Swapping Station

For a successful rollout of electric vehicles (EVs), it is required to establish an adequate charging infrastructure. The adequate access to such infrastructure would help to mitigate concerns associated with limited EV range and long charging times. Battery swapping stations are poised as effective means of eliminating the long waiting times associated with charging the EV batteries. These stations are mediators between the power system and their customers. In order to successfully deploy this type of stations, a business and operating model is required, that will allow it to generate profits while offering a fast and reliable alternative to charging batteries. This paper proposes an optimization framework for the operating model of battery swapping stations. The proposed model considers the day-ahead scheduling process. Battery demand uncertainty is modeled using inventory robust optimization, while multi-band robust optimization is employed to model electricity price uncertainty. The results show the viability of the proposed model as a business case, as well as the effectiveness of the model to provide the required service.

Toward Cost-Efficient and Reliable Unit Commitment Under Uncertainty

Large-scale integration of wind farms causes volatile bus net injections. Although these fluctuations are anticipated, their timing, magnitude and duration cannot be predicted accurately. In order to maintain the operational reliability of the system, this uncertainty must be adequately addressed at the day-ahead generation scheduling stage. The ad-hoc reserve rules incorporated in deterministic unit commitment formulations do not adequately account for this uncertainty. Scenario-based stochastic unit commitment formulations model this uncertainty more precisely, but require computationally demanding simulations. Interval and robust optimization techniques require less computing resources, but produce overly conservative and thus expensive generation schedules. This paper proposes a transmission-constrained unit commitment formulation that improves the performance of the interval unit commitment. The uncertainty is modeled using upper and lower bounds, as in the interval formulation, but inter-hour ramp requirements are based on net load scenarios. This improved interval formulation has been tested using the IEEE RTS-96 and compared with existing stochastic, interval and robust unit commitment techniques in terms of solution robustness and cost. These results show that the proposed method outperforms the existing interval technique both in terms of cost and computing time.

Yearly Maintenance Scheduling of Transmission Lines Within a Market Environment

Within a yearly horizon, a transmission system operator needs to schedule the maintenance outages of the set of transmission lines due for maintenance. Facing this task, two conflicting objectives arise: on one hand, the transmission system adequacy should be preserved as much as possible, and, on the other hand, market operation should be altered in the least possible manner. To address this scheduling problem, a bilevel model is proposed whose upper-level problem schedules line maintenance outages pursuing maximum transmission capacity margin. This upper-level problem is constrained by a set of lower-level problems that represent the clearing of the market for all the time periods considered within the yearly planning horizon. This bilevel model is conveniently converted into a nonlinear mathematical program with equilibrium constraints (MPEC) that can be recast as a mixed-integer linear programming problem solvable with currently available branch-and-cut techniques.

Comparison of state-of-the-art transmission constrained unit commitment formulations

The paper reviews and compares several mixed-integer linear programming formulations of the transmission constrained unit commitment problem. It analyzes these formulations and provides extensions that properly address all the generator initial conditions. Computing times to solve the unit commitment problem using these different formulations are analyzed and compared. Tests were performed using a version of the IEEE RTS-96 system that has been updated to include generator types that better reflect contemporary generation mixes. Detailed data on all the power system parameters are provided, including initial generator conditions.

Coupling Pumped Hydro Energy Storage With Unit Commitment

Renewable electricity generation not only provides affordable and emission-free electricity but also introduces additional complexity in the day-ahead planning procedure. To address the stochastic nature of renewable generation, system operators must schedule enough controllable generation to have the flexibility required to compensate unavoidable real-time mismatches between the production and consumption of electricity. This flexibility must be scheduled ahead of real-time and comes at a cost, which should be minimized without compromising the operational reliability of the system. Energy storage facilities, such as pumped hydro energy storage (PHES), can respond quickly to mismatches between demand and generation. Hydraulic constraints on the operation of PHES must be taken into account in the day-ahead scheduling problem, which is typically not done in deterministic models. Stochastic optimization enhances the procurement of flexibility, but requires more computational resources than conventional deterministic optimization. This paper proposes a deterministic and an interval unit commitment formulation for the co-optimization of controllable generation and PHES, including a representation of the hydraulic constraints of the PHES. The proposed unit commitment (UC) models are tested against a stochastic UC formulation on a model of the Belgian power system to compare the resulting operational cost, reliability, and computational requirements. The cost-effective regulating capabilities offered by the PHES yield significant operational cost reductions in both models, while the increase in calculation times is limited.

Enhanced Security-Constrained Unit Commitment With Emerging Utility-Scale Energy Storage

We introduce emerging utility-scale energy storage (e.g., batteries) as part of the set of control measures in a corrective form of the security-constrained unit commitment (SCUC) problem. This enhanced SCUC (ESCUC) leverages utility-scale energy storage for multiple applications. In the base case, the storage units are optimally charged and discharged to realize economic operation. Immediately following a contingency, the injections of storage units are adjusted almost instantly to alleviate short-term emergency overloads, thereby avoiding potential cascading outages and giving slow ramping generating units time to adjust their output. The ESCUC is a large two-stage mixed-integer programming problem. A Benders decomposition has been developed to solve this problem. In order to achieve computational tractability, we present several acceleration techniques to improve the convergence of the proposed algorithm. Case studies on the RTS-79 and RTS-96 systems demonstrate the effectiveness of the proposed approach.

Comparison of scenario reduction techniques for the stochastic unit commitment

A number of scenario reduction techniques have been proposed to make possible the practical implementation of stochastic unit commitment formulations. These scenario-reduction techniques aggregate similar scenarios based on their metrics, such as their probability, hourly magnitudes, or the cost resulting from each scenario. This paper compares these different scenario reduction techniques in terms of the resulting operating cost and the amount of time required to complete computation of the stochastic UC. This comparison is based on Monte Carlo simulations of the resulting generation schedules for a modified version of the 24-bus IEEE-RTS.

An EPEC approach to the yearly maintenance scheduling of generating units

This paper considers the yearly maintenance scheduling of generating units within a market environment. Each producer schedules its units maintenance periods to maximize its revenue using a bilevel approach. The upper-level problem of this bilevel model seeks maximum revenue and contains unit scheduling constraints, while the lower-level problems represent the market clearing process under different operating conditions. This single producer maintenance problem can be recast as a mathematical program with equilibrium constraints (MPEC). Since the MPECs of all producers have to be considered simultaneously and the market clearing process is common to all of them, the proposed formulation for maintenance scheduling is an equilibrium problem with equilibrium constraints (EPEC) corresponding to a multiple-leader-common-follower game. The solution of this EPEC is a set of equilibria, in which none of the producers is able to increase its revenue unilaterally by changing the maintenance periods of its generating units.