Deep decarbonization of electricity production is a societal challenge that can be achieved with high penetrations of variable renewable energy. We investigate the potential of energy storage technologies to reduce renewable curtailment and CO2emissions in California and Texas under varying emissions taxes. We show that without energy storage, adding 60 GW of renewables to California achieves 72% CO2reductions (relative to a zero-renewables case) with close to one third of renewables being curtailed. Some energy storage technologies, on the other hand, allow 90% CO2reductions from the same renewable penetrations with as little as 9% renewable curtailment. In Texas, the same renewable-deployment level leads to 54% emissions reductions with close to 3% renewable curtailment. Energy storage can allow 57% emissions reductions with as little as 0.3% renewable curtailment. We also find that generator flexibility can reduce curtailment and the amount of energy storage that is needed for renewable integration.
Stochastic Planning of a Mostly-Renewable Power Grid
Power grid resource adequacy can be difficult to ensure with high penetrations of intermittent renewable energy. We explore enhancing resource adequacy by overbuilding renewables while modeling statistical correlations in renewable power at different sites. Overbuilding allows production during times of low power, and exploiting statistical correlations can reduce power variability and, subsequently, reduce needed renewable capacity. In this work, we present a stochastic optimization problem to size renewables and expand transmission while minimizing the expected dispatch cost. Our method uses statistical profiles of renewable production and embeds network constraints using the DC power flow equations. We assess our method’s effects on feasibility, load shedding, locational marginal prices, and generator curtailment. On the IEEE 9-bus system, we found that anti-correlation between generators reduced generation capacity needs with sufficient transmission. On the IEEE 30-bus system, we found that the optimal solution required significant overbuilding and curtailment of renewables regardless of the marginal cost of schedulable generation.
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- Award ID(s):
- 1845093
- PAR ID:
- 10486458
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Conference on Control Technology and Applications (CCTA)
- ISBN:
- 979-8-3503-3544-6
- Page Range / eLocation ID:
- 25 to 31
- Format(s):
- Medium: X
- Location:
- Bridgetown, Barbados
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Despite the increasing level of renewable power generation in power grids, fossil fuel power plants still have a significant role in producing carbon emissions. The integration of carbon capturing and storing systems to the conventional power plants can significantly reduce the spread of carbon emissions. In this paper, the economic-emission dispatch of combined renewable and coal power plants equipped with carbon capture systems is addressed in a multi-objective optimization framework. The power systems flexibility is enhanced by hydropower plants, pumped hydro storage, and demand response program. The wind generation and load consumption uncertainties are modeled using stochastic programming. The DC power flow model is implemented on a modified IEEE 24-bus test system. Solving the problem resulted in an optimal Pareto frontier, while the fuzzy decision-making method found the best solution. The sensitivity of the objective functions concerning the generation-side is also investigated.more » « less
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The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of power system planning and operation. Facing these challenges, this paper proposes a cost-effective, nonparametric method to quantity the impact of uncertain power injections on the load margins. First, we propose to generate system uncertain inputs via a novel vine copula due to its capability in simulating complex multivariate highly dependent model inputs. Furthermore, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. The simulations conducted on the IEEE 57-bus system, to which correlated renewable generation are attached, reveal the excellent performance of the proposed method.more » « less
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The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of power system planning and operation. Facing these challenges, this paper proposes a cost-effective, nonparametric method to quantify the impact of uncertain power injections on the load margins. First, we propose to generate system uncertain inputs via a novel vine copula due to its capability in simulating complex multivariate highly dependent model inputs. Furthermore, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. The simulations conducted on the IEEE 57-bus system, to which correlated renewable generation are attached, reveal the excellent performance of the proposed method.more » « less
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As the level of uncertain renewable capacity increases on power systems worldwide, industrial and academic researchers alike are seeking a scalable, transparent, effective approach to unit commitment under uncertainty. This paper presents a statistical ranking methodology that allows adaptive robust stochastic unit commitment using a modular structure, with much-needed flexibility. Specifically, this work describes a bus ranking methodology that identifies the most critical buses based on a worst-case metric. An important innovation is the ability to identify alternative metrics on which to rank the uncertainty set -- for example to minimize economic dispatch cost or ramping needs, to provide a customized robust unit commitment solution. Compared to traditional robust unit commitment models, the proposed model combines statistical tools with analytical framework of power system networks. The resulting formulation is easily implementable and customizable to the needs of the system operator. The method and its applications are validated against other established approaches, showing equivalent solution to the state-of-the-art approach. Case studies were conducted on the IEEE-30, IEEE-118 and pegase-1354 networks. In addition, the flexibility of bus ranking formulation is illustrated through implementation of alternative definitions of worst-case metrics. Results show that the bus ranking method performs as well as the best of these methods, with the provision of additional flexibility.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCTA54093.2023.10253007
