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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]

Distributed transmission-scale energy storage is becoming economically feasible due to the growing share of renewable generation and cost reduction of specific storage technologies, primarily batteries. Under these circumstances, independent merchants may start investing in storage facilities. On the other hand, system operators, besides investing in transmission lines, may, under certain conditions, invest in storage units as well. This paper formulates a trilevel model where the upper-level problem optimizes the system operator's transmission line and energy storage investments, the middle-level problem determines merchant energy storage investment decisions, while the lower level problem simulates market clearing process for representative days. After replacing the lower level problem with its primal dual equivalent conditions, the middle- and lower level problems are merged into a mixed-integer problem with equilibrium constraints. The resulting bilevel structure is iteratively solved using a cutting plane algorithm. The proposed formulation is first applied to a six-bus system to present the mechanics of the model and then to the IEEE RTS-96 test system. The results show that even at the low cost of energy storage, the system operator (SO) still prefers line investments, while merchant investments are driven by the volatility of LMPs. Both the SO and merchant investments increase the social welfare, although this increase is mostly driven by the SO investments. [ABSTRACT FROM AUTHOR]

Massive integration of renewable energy resources calls for new operating and planning paradigms, which address reduced controllability and increased uncertainty on the generation side. On the other hand, emerging energy storage technologies can provide additional flexibility. Therefore, generation and storage expansion models need to be coordinated to ensure sufficiency of system-wide response capabilities within different regulation intervals. This paper proposes a coordinated generation and storage expansion formulation considering primary frequency response constraints. This is a stochastic mixed-integer linear program solved using an off-the-shelf solver. The proposed formulation is compared to the case when primary frequency response is neglected. The case study performed for an eight-zone ISO New England test system quantifies the value of energy storage simultaneously providing primary frequency response and spatio-temporal arbitrage. [ABSTRACT FROM AUTHOR]

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. [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]

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]