Scenario-Based Economic Dispatch With Tunable Risk Levels in High-Renewable Power Systems

Modarresi, Mohammad Sadegh; Xie, Le; Campi, Marco C.; Garatti, Simone; Carè, Algo; Thatte, Anupam A.; Kumar, P. R.

doi:10.1109/tpwrs.2018.2874464

Cited by 55 publications

(18 citation statements)

References 53 publications

(70 reference statements)

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“…It has been applied on various power system problems, e.g. economic dispatch [21] and demand response [22]. The scenario approach utilizes N independent and identically distributed (i.i.d.)…”

Section: B Scenario Approachmentioning

confidence: 99%

Chance-constrained Unit Commitment via the Scenario Approach

Geng

Xie

2019

2019 North American Power Symposium (NAPS)

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Keeping the balance between supply and demand is a fundamental task in power system operational planning practices. This task becomes particularly challenging due to the deepening penetration of renewable energy resources, which induces a significant amount of uncertainties. In this paper, we propose a chance-constrained Unit Commitment (c-UC) framework to tackle challenges from uncertainties of renewables. The proposed c-UC framework seeks cost-efficient scheduling of generators while ensuring operation constraints with guaranteed probability. We show that the scenario approach can be used to solve c-UC despite of the non-convexity from binary decision variables. We reveal the salient structural properties of c-UC, which could significantly reduce the sample complexity required by the scenario approach and speed up computation. Case studies are performed on a modified 118-bus system. The authors are with the Recently, the growing amount of uncertainties from renewables pose new challenges on the operations of power systems. UC, as a critical part of day-ahead scheduling, needs to be improved to consider the impacts of uncertainties. Broadly speaking, there are two approaches for decision making under uncertainties: stochastic optimization (SO) and robust optimization (RO). SO relies on probabilistic models to explain uncertainties and often optimizes the objective function in the presence of randomness. SO has found many successful applications in power systems. References [1]-[3] formulate and solve the stochastic unit commitment problem, which typically minimizes expected commitment and dispatch costs in the presence of uncertainties. RO takes an alternative approach, in which the uncertainty model is set-based and deterministic. Recently, researchers in [4] formulated and solved the robust unit commitment problem, which minimizes the commitment and dispatch costs for the worst case in a predefined uncertainty set. Both approaches attract a lot of attention and are relatively successful in addressing the challenges related with uncertainties. This paper looks at the UC problem through the lens of chance-constrained optimization (CCO), which is closely related with both stochastic and robust optimization [5]. The main difference of CCO from SO or RO is the chance constraint (i.e. (1b) and (2b)), which explicitly considers the feasibility of solutions under uncertainties.Various formulations of chance-constrained (security-constrained) unit commitment have been proposed, e.g.[6]- [14]. As mentioned in [5], chance-constrained optimization problems can be solved via different methods. We take chance-constrained unit commitment problem as an example. It can be solved using sample average approximation [8]-[10], [12]-[14] or robust optimization based techniques [15]. The scenario approach, which might be the most wellknown method to solve chance-constrained optimization, was not directly applied on the unit commitment. The only related references we found are [16], [17], which are built upon a variation of the scen...

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Section: B Scenario Approachmentioning

confidence: 99%

Chance-constrained Unit Commitment via the Scenario Approach

Geng

Xie

2019

2019 North American Power Symposium (NAPS)

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“…This is the approach followed for instance in [91]- [93], [97]. In particular, in the field of the scenario based DC-OPF, a very promising approach has been recently proposed in [74]. In [74], two approaches to solve DC-OPF via scenario are presented.…”

Section: ) Dc-based Approaches: Most Literature On Uncertain Opf Getmentioning

confidence: 99%

“…In particular, in the field of the scenario based DC-OPF, a very promising approach has been recently proposed in [74]. In [74], two approaches to solve DC-OPF via scenario are presented. First neglecting transmission constraints, it was shown that the number of i.i.d.…”

Section: ) Dc-based Approaches: Most Literature On Uncertain Opf Getmentioning

confidence: 99%

Algorithms for OptimalACPower Flow in the Presence of Renewable Sources

Chamanbaz

Dabbene

Lagoa

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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This chapter presents recent solutions to the optimal power flow (OPF) problem in the presence of renewable energy sources (RES), such as solar photo-voltaic and wind generation. After introducing the original formulation of the problem, arising from the combination of economic dispatch and power flow, we provide a brief overview of the different solution methods proposed in the literature to solve it. Then, we explain the main difficulties arising from the increasing RES penetration, and the ensuing necessity of deriving robust solutions. Finally, we present the state-of-the-art techniques, with a special focus on recent methods we developed, based on the application on randomization-based methodologies.

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“…Therefore, we only need to generate samples of |V | for the scenario optimization, see later in (13). Moreover, since |V | appears as an additive term in (10), this simplifies the process of identifying support scenarios and scenarios to remove, as we will see later in (14). An objective for the optimal power flow could be to maximize the injection of distributed energy sources, and thus minimize the energy required from the grid, while satisfying the voltage limits V min , V max :…”

Section: Power System Applicationmentioning

confidence: 99%

“…In [8], [9], [10] this is achieved assuming a known probability distribution of these uncertainties. In [11], [12], [13], [14], [15] this assumption is dropped, and the scenario approach is used as an appropriate tool to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Sieving out Unnecessary Constraints in Scenario Optimization with an Application to Power Systems

Picallo

Dörfler

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Many optimization problems incorporate uncertainty affecting their parameters and thus their objective functions and constraints. As an example, in chance-constrained optimization the constraints need to be satisfied with a certain probability. To solve these problems, scenario optimization is a well established methodology that ensures feasibility of the solution by enforcing it to satisfy a given number of samples of the constraints. The main theoretical results in scenario optimization provide the methods to determine the necessary number of samples, or to compute the risk based on the number of so-called support constraints. In this paper, we propose a methodology to remove constraints after observing the number of support constraints and the consequent risk. Additionally, we show the effectiveness of the approach with an illustrative example and an application to power distribution grid management when solving the optimal power flow problem. In this problem, uncertainty in the loads converts the admissible voltage limits into chance-constraints.

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Scenario-Based Economic Dispatch With Tunable Risk Levels in High-Renewable Power Systems

Cited by 55 publications

References 53 publications

Chance-constrained Unit Commitment via the Scenario Approach

Chance-constrained Unit Commitment via the Scenario Approach

Algorithms for OptimalACPower Flow in the Presence of Renewable Sources

Sieving out Unnecessary Constraints in Scenario Optimization with an Application to Power Systems

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