Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

Edlund, Kristian; Bendtsen, Jan Dimon; Børresen, Simon; Mølbak, Tommy

doi:10.3182/20080706-5-kr-1001.01184

Cited by 12 publications

(9 citation statements)

References 4 publications

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“…This case study is used to illustrate the properties and potential of Economic MPC in managing the power production and consumption in a distributed energy system. Compared to the studies in [8], [9], [16], the novelty in this paper is inclusion of a controllable power consumer to shed the power load.…”

Section: Models For a Power Systemmentioning

confidence: 99%

“…Model Predictive Control (MPC) for constrained systems has emerged during the last 30 years as the most successful methodology for control of industrial processes [2]- [4]. MPC is increasingly being considered for refrigeration systems [5]- [7] and for power production plants [8], [9]. Traditionally, MPC is designed using objective functions penalizing deviations from a given set-point.…”

Section: Introductionmentioning

confidence: 99%

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The potential of Economic MPC for power management

Hovgaard

Edlund

Jørgensen

2010

49th IEEE Conference on Decision and Control (CDC)

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Abstract-Economic Model PredictiveControl is a receding horizon controller that minimizes an economic objective function rather than a weighted least squares objective function as in Model Predictive Control (MPC). We use Economic MPC to operate a portfolio of power generators and consumers such that the cost of producing the required power is minimized. The power generators are controllable power generators such as combined heat and power generators (CHP), coal and gas fired power generators, as well as a significant share of uncontrollable power generators such as parks of wind turbines. In addition, some of the power consumers are controllable. In this paper, the controllable power consumers are exemplified by large cold rooms or aggregations of super markets with refrigeration systems. We formulate the Economic MPC as a linear program. By simulation, we demonstrate the performance of Economic MPC for a small conceptual example.

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Section: Models For a Power Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The potential of Economic MPC for power management

Hovgaard

Edlund

Jørgensen

2010

49th IEEE Conference on Decision and Control (CDC)

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“…By simulation Model Predictive Control has been demonstrated as a very promising technology for dynamic regulation and coordination of power generation in the DONG Energy portfolio [3]. This controller is called the DONG Energy portfolio balance controller.…”

Section: A Power Portfolio Controlmentioning

confidence: 99%

“…The problem and the models are described in detail in [3], [15]. The power balance controller is a Model Predictive Controller in which a constrained optimal control problem is solved at each sampling instant.…”

Section: Problem Definitionmentioning

confidence: 99%

“…In this paper we develop a primaldual interior-point algorithm for model predictive control (MPC) with input and input-rate constraints and an objective function consisting of linear stage costs as well as l 1 -norms penalizing deviation from target and movements [1], [2], [3]. The primal-dual interior point algorithm is based on Mehrotra's predictor-corrector algorithm [4], [5], [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

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A primal-dual interior-point linear programming algorithm for MPC

Edlund

Sokoler

Jørgensen

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Abstract-Constrained optimal control problems for linear systems with linear constraints and an objective function consisting of linear and l1-norm terms can be expressed as linear programs. We develop an efficient primal-dual interior point algorithm for solution of such linear programs. The algorithm is implemented in Matlab and its performance is compared to an active set based LP solver and linprog in Matlab's optimization toolbox. Simulations demonstrate that the new algorithm is more than one magnitude faster than the other LP algorithms applied to this problem.

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