Multiple Time Resolution Stochastic Scheduling for Systems with High Renewable Penetration (original) (raw)
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IEEE Transactions on Power Systems, 2000
This paper introduces the idea of unified unit commitment and economic dispatch modeling within a unique tool that performs economic dispatch with up to 24-hour look-ahead capability. The tool provides financially binding dispatch and ex-ante locational marginal prices (LMPs) for the next 5-min interval and advisory commitment, dispatch schedule and prices for the remaining scheduling horizon. Variable time resolution and variable modeling complexity are used in order to reduce computational requirements. A finer time resolution and detailed modeling are used during the first hours of the scheduling horizon while coarser time resolution and simplified modeling during the last ones. The viability of the method for medium-sized systems is demonstrated through its application to the Greek power system.
Storage management by rolling stochastic unit commitment for high renewable energy penetration
Electric Power Systems Research, 2018
This paper presents a unified unit commitment and economic dispatch model that integrates storage devices for the short-term operations scheduling of power systems with high renewable penetration. The presented model is a single multi-hour look-ahead real-time tool that uses multiple time resolution to contain computational requirements. The decisions for the first time interval are binding while the decisions of the remaining scheduling horizon are advisory. Adopting this approach, storage facilities are more efficiently utilized, by constantly adapting their energy injection/withdrawal schedule based on updated system information, thus, alleviating the problem of defining the appropriate stored energy level during economic dispatch. The proposed model is presented in both deterministic and stochastic frameworks. The operational impacts of storage and the benefits of implementing stochastic optimization are validated via extensive simulations using data from the Greek Interconnected Power System.
This paper proposes a methodology to model and solve the problem of stochastic economic dispatch incorporating renewable energies. In this context, demand and generation randomness (wind speed, solar radiation and rates of failure) are considered. Demand, wind speed, solar radiation and unavailability are modeled through Normal, Weibull, Beta and Uniform distributions respectively. The problem is therefore recognized as a stochastic process. Consequently, the cost of load shedding is considered. In order to define the optimal power allocation for each generator, the proposed methodology uses Group SO (3) orthogonal matrices (Lie's algebra), the marginal costs of the generators, the customer damage cost and Monte-Carlo trials. The result contains generation, marginal cost and load shedding statistics, among others.
Energies, 2017
The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-ahead scheduling results are tested against different real-time dispatch regimes, with none or limited look-ahead capability, or with the use of the variability reserve, utilizing a modified version of the Greek power system. The results demonstrate the enhanced reliability achieved by applying the multi-timing scheduling concept and explicitly considering the variability reserve, and certain features regarding the allocation and deployment of reserves are discussed.
Comparison of advanced power system operations models for large-scale renewable integration
Electric Power Systems Research, 2015
Increased renewable energy sources (RES) penetration requires significant changes in the short-term power system operations practice. Both current industry practices and relevant literature investigate models that operate on variable time scales to address RES uncertainty and variability. This paper presents a comparison of three different integrated short-term power system operations models regarding their ability to deal with large amounts of renewable penetration. The first model is a rolling unified unit commitment-economic dispatch (UUCED) model with variable time resolution, recently introduced by the authors. The second scheduling model comprises a rolling intraday unit commitment and a realtime dispatch with look-ahead capability (two-level model). The third model operates the system on a three-level hierarchy: it comprises a 48-h reliability unit commitment (deterministic or stochastic), a rolling intraday unit commitment and a real-time dispatch with look-ahead capability. The comparison is performed on the basis of an annual simulation of the Greek Interconnected Power System using 2013 historic wind power and load data. Simulation results demonstrate that the UUCED model better accommodates the increasing RES production by minimizing the system operating costs without jeopardizing system security.
Stochastic Real-Time Scheduling of Wind-Thermal Generation Units in an Electric Utility
The objective of the dynamic economic dispatch (DED) problem is to find the optimal dispatch of generation units in a given operation horizon to supply a prespecified demand while satisfying a set of constraints. In this paper, an efficient method based on optimality-condition-decomposition technique is proposed to solve the DED problem in real-time environment while considering wind power generation and pool market. The uncertainties of wind power generation, as well as the electricity prices, are also taken into account. The aforementioned uncertainties are handled using a scenario-based approach. To illustrate the effectiveness of the proposed approach, it is applied on 40 and 54 thermal generation units and a large-scale practical system with 391 thermal generation units. The obtained results substantiate the applicability of the proposed method for solving the real-time DED problem with uncertain wind power generation.
Electric Power Systems Research, 2015
In this paper a probabilistic economic dispatch model considering thermal units (fuel generators), photovoltaic arrays and wind energy conversion systems is proposed. Wind speed, solar radiation and power demand are recognized as random variables. Unavailability of each type of power source is also considered. The solution strategy is based on the Monte Carlo method and non-linear constrained optimization. The optimal solution involves single and multidimensional probabilities, descriptive statistics, cluster and bimodal analysis. The proposed methodology yields the probability distributions of system marginal price, thermal (fuel based), solar and wind power generation and load shedding. The proposed model and methodology are applied to a case study of the Northern Chilean electrical system.
Real-time dynamic economic load dispatch integrated with renewable energy curtailment
Journal of International Council on Electrical Engineering, 2019
Recent power system operations are faced with the difficulties due to large penetration levels of intermitted renewable energy sources (RESs), for example, photovoltaic power (PV) generations and wind-turbine (WT) generations. Operations of a large number of uncontrollable RES also cause an unforeseen issue for the reliability in power system operation. To tackle the issue, this paper proposes a novel real-time generation schedule (GS) integrated with RES' power curtailment. The proposed real-time GS become to effectively compute the outputs of the associated generating units so as to meet the required electricity load. The GS computation is frequently updated every 5-min in real-time operation. The proposed approach is applied for real-time dynamic economic load dispatch for a real-time GS that completely treats the limited controllable energy resources under the uncertainties. Furthermore, real-time PV forecasting with their covariance matrices of the prediction errors is effectively utilized in the approach. In case of a unforeseen situation when the estimated electricity load cannot match the controllable unit's capability, the approach can be detect a minimum amount of supply-demand mismatch in advance, and can manage it reliably for the considerable time horizon.
Energies
In the context of the growing penetration of renewable power sources in power systems causing probabilistic contingency conditions, a suitable economic dispatch model is decisively needed. There is a lack of research in the field of probabilistic mathematical formulation considering the uncertainties due to the stochastic nature of renewables and contingency occurrence, as it is a very complex problem to be solved. The most appropriate model is the stochastic security-constrained economic dispatch (SSCED) model for optimized economic dispatch decisions during uncertainty. However, because of its complexity, it is rarely employed. This paper attempts to solve the complex SSCED problem in the presence of the uncertainty of resources and probabilistic contingency conditions, which is a novel effort in this regard. The SSCED is carried out over multiple periods to provide the load-following or contingency reserves. In the proposed SSCED, the uncertainty problem is addressed by modeling ...
Stochastic Optimization of Sub-Hourly Economic Dispatch With Wind Energy
IEEE Transactions on Power Systems, 2016
We present a stochastic programming framework for a multiple timescale economic dispatch problem to address integration of renewable energy resources into power systems. This framework allows certain slow-response energy resources to be controlled at an hourly timescale, while fast-response resources, including renewable resources, and related network decisions can be controlled at a sub-hourly timescale. To this end, we study two models motivated by actual scheduling practices of system operators. Using an external simulator as driver for sub-hourly wind generation, we optimize these economic dispatch models using stochastic decomposition, a sample-based approach for stochastic programming. Computational experiments, conducted on the IEEE-RTS96 system and the Illinois system, reveal that optimization with sub-hourly dispatch not only results in lower expected operational costs, but also predicts these costs with far greater accuracy than with models allowing only hourly dispatch. Our results also demonstrate that when compared with standard approaches using the extensive formulation of stochastic programming, the sequential sampling approach of stochastic decomposition provides better predictions with much less computational time.