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Modern mixed-integer quadratic solvers generally handle binary variables more efficiently than nonlinear mixed-integer solvers. This is relevant to the power system operation models as the unit commitment formulations typically contain a large number of binary variables. This paper investigates how to achieve the accuracy level close to the one of the exact nonlinear models, but by utilising convex models and solvers. The presented unit commitment model is based on a Taylor-series expansion where both the voltage magnitude and angle are quadratically constrained. To achieve high accuracy, the model takes advantage of the meshed transmission network structure that enables replacement of the quadratic inequality constraints that cause constraint relaxation errors with the linear equality constraints. Quadratic constraints to be replaced as well as the operating point parameters are determined based on the presolve. The first presented case study validates the model's accuracy and the convergence of the iterative algorithm, while the second is a non-iterative full unit commitment problem. Unit commitment results show superior accuracy and similar computation times to the existing quadratic formulations on one hand and faster computation times than the exact nonlinear polar formulation on the other. [ABSTRACT FROM AUTHOR]

To decarbonize the heating sector, residential consumers may install heat pumps. Coupled with heating loads with high thermal inertia, these thermostatically controlled loads may provide a significant source of demand side flexibility. Since the capacity of residential consumers is typically insufficient to take part in the day-ahead electricity market (DAM), aggregators act as mediators that monetize the flexibility of these loads through demand response (DR). In this paper, we study the strategic interactions between an aggregator, its consumers and the DAM using a bilevel optimization framework. The aggregator-consumer interaction is captured either as a Stackelberg or a Nash Bargaining Game, leveraging chance-constrained programming to model limited controllability of residential DR loads. The aggregator takes strategic positions in the DAM, considering the uncertainty on the market outcome, represented as a stochastic Stackelberg Game. Results show that the DR provider-aggregator cooperation may yield significant monetary benefits. The aggregator cost-effectively manages the uncertainty on the DAM outcome and the limited controllability of its consumers. The presented methodology may be used to assess the value of DR in a deregulated power system or may be directly integrated in the daily routine of DR aggregators. [ABSTRACT FROM AUTHOR]

Suitably located energy storage systems are able to collect significant revenue through spatiotemporal arbitrage in congested transmission networks. However, transmission capacity expansion can significantly reduce or eliminate this source of revenue. Investment decisions by merchant storage operators must, therefore, account for the consequences of potential investments in transmission capacity by central planners. This paper presents a tri-level model to co-optimize merchant electrochemical storage siting and sizing with centralized transmission expansion planning. The upper level takes the merchant storage owner's perspective and aims to maximize the lifetime profits of the storage, while ensuring a given rate of return on investments. The middle level optimizes centralized decisions about transmission expansion. The lower level simulates market clearing. The proposed model is recast as a bi-level equivalent, which is solved using the column-and-constraint generation technique. A case study based on a 240-bus, 448-line testbed of the Western Electricity Coordinating Council interconnection demonstrates the usefulness of the proposed tri-level model. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

This paper describes a robust optimization approach to minimize the total cost of supplying a remote telecommunication station exclusively by renewable energy sources (RES). Due to the intermittent nature of RES, such as photovoltaic (PV) panels and small wind turbines, they are normally supported by a central energy storage system (ESS), consisting of a battery and a fuel cell. The optimization is carried out as a robust mixed-integer linear program (RMILP), and results in different optimal solutions, depending on budgets of uncertainty, each of which yields different RES and storage capacities. These solutions are then tested against a set of possible outcomes, thus simulating the future operation of the system. Since battery cycling is inevitable in this application, an algorithm that counts the number of cycles and associated depths of discharges (DoD) is applied to the optimization results. The annual capacity reduction that results from these cycles is calculated for two types of battery technologies, i.e., valve-regulated lead–acid (VRLA) and lithium–ion (Li–ion), and treated as an additional cost. Finally, all associated costs are added up and the ideal configuration is proposed. [ABSTRACT FROM PUBLISHER]