Rm Ralph Hermans

Assessment of non-centralised model predictive control techniques for electrical power networks

Model predictive control (MPC) is one of the few advanced control methodologies that have proven to be very successful in real-life applications. An attractive feature of MPC is its capability of explicitly taking state and input constraints into account. Recently, there has been an increasing interest in the usage of MPC schemes to control electrical power networks. The major obstacle for implementation lies in the large scale of these systems, which is prohibitive for a centralised approach. In this article, we therefore assess and compare the suitability of several non-centralised predictive control schemes for power balancing, to provide valuable insights that can contribute to the successful implementation of non-centralised MPC in the real-life electrical power system.

Incentive-based coordinated charging control of plug-in electric vehicles at the distribution-transformer level

Distribution utilities are becoming increasingly aware that their networks may struggle to accommodate large numbers of plug-in electric vehicles (PEVs). In particular, uncoordinated overnight charging is expected to be problematic, as the corresponding aggregated power demand exceeds the capacity of most distribution substation transformers. In this paper, a dynamical model of PEVs served by a single temperature-constrained substation transformer is presented and a centralized scheduling scheme is formulated to coordinate charging of a heterogeneous PEV fleet. We employ the dual-ascent method to derive an iterative, incentive-based and non-centralized implementation of the PEV charging algorithm, which is optimal upon convergence. Then, the distributed open-loop problem is embedded in a predictive control scheme to introduce robustness against disturbances. Simulations of an overnight charging scenario illustrate the effectiveness of the so-obtained incentive-based coordinated PEV control scheme in terms of performance and enforcing the transformers thermal constraint.

Almost decentralized lyapunov-based nonlinear model predictive control

This paper proposes an almost decentralized solution to the problem of stabilizing a network of discrete-time nonlinear systems with coupled dynamics that are subject to local state/input constraints. By “almost decentralized” we mean that each local controller is allowed to use the states of neighboring systems for feedback, whereas it is not permitted to employ iterations between the systems in the network to compute the control action. The controller synthesis method used in this work is Lyapunov-based model predictive control (MPC). The stabilization conditions are decentralized via a set of structured control Lyapunov functions (CLFs) for which the maximum over all the functions in the set is a CLF for the global network of systems. However, this does not necessarily imply that each function is a CLF for its corresponding subsystem. Additionally, we provide a solution for relaxing the temporal monotonicity of the CLF for the overall network. For structured CLFs defined using the infinity norm, we show that the decentralized MPC algorithm can be implemented by solving a single linear program in each network node. A nontrivial example illustrates the effectiveness of the developed theory and shows that the proposed method can perform as well as more complex distributed, iteration-based MPC algorithms.

Low-complexity model predictive control of electromagnetic actuators with a stability guarantee

Electromagnetically driven mechanical systems are characterized by fast nonlinear dynamics that are subject to physical and control constraints, which makes controller design a challenging problem. This paper presents a novel model predictive control (MPC) scheme that can handle both the performance/physical constraints and the strict limits on computational complexity required in control of general electromagnetic (EM) actuators. The novel aspects of the MPC design are a one-step-ahead prediction horizon and an infinity-norm artificial Lyapunov function that is employed to drive the system to a desired reference. An additional optimization variable is introduced to relax the conditions on the Lyapunov function, which is not forced to decrease monotonically. In this way feasibility of the MPC algorithm is improved considerably. While the MPC scheme uses a full nonlinear model, which improves performance, we show that the resulting MPC problem can still be transformed into a low-complexity linear program that can be solved by modern microprocessors within tenths of milliseconds. Moreover, an even simpler piecewise affine explicit controller can be obtained via multiparametric programming. Simulation results are reported and compared with the results achieved by state-of-the-art explicit MPC based on a piecewise affine model.

Distributed economic model predictive control of networks in competitive environments

This paper addresses the control of large-scale networks of dynamical systems with a certain global objective that needs to be pursued through the actions of individual competing agents. In particular, we consider the stabilization of a specific network output at zero. This is challenging as each agent is interested only in its own objectives such as the maximization of its economic profit. We introduce a control scheme based on the economic Model Predictive Control (MPC) theory to optimize economic performance of the competing agents. The theory is then modified to take global, network-wide objectives into account as well. Additionally, it is shown that the control scheme can be distributed between the agents using the dual decomposition technique such that exchange of confidential information among these competitors is not required. Simulation results for a power system example illustrate the potential of the control strategy in terms of ensuring stable and economical operation of networks in competitive environments.

Distributed, price-based control approach to market-based operation of future power systems

In this paper we present, discuss and illustrate on examples the price-based control paradigm as a suitable approach to solve some of the challenging problems facing future, market-based power systems. It is illustrated how global objectives and constraints are optimally translated into time-varying prices. The real-time varying price signals are guaranteed to adequately reflect the state of the physical system and present the signals that optimally shape, coordinate and synchronize local, profit driven behaviors of producers/consumers to mutually reinforce and guarantee global objectives and constraints. As an illustrative example, the real-time pricebased power balance control with congestion management is presented.

Combined bidding at power and ancillary service markets

Power and ancillary service markets are strongly coupled. However, at the moment auctions are organized in such a manner that the coupling is not taken into account. Every market participant submits bids at power and/or AS markets without possibility to adjust with respect to the outcome of the other market. Inexact approximations of the actual market price induce deviations from the optimal social welfare value. In this paper, we firstly describe and analyze the consequences of power and ancillary service market coupling. Secondly, we present two different market design strategies, both of which are based on the idea of iterative auction and have the goal to optimally account for power and AS coupling, enabling the overall system to maximize its social welfare. An illustrative example is used to present potential benefits and downsides that might arise as a result of introducing proposed market arrangements.

Distributed predictive control of the 7-machine CIGRÉ power system

Stable operation of the future electrical power system will require efficient techniques for supply-demand balancing, i.e., load-frequency control, due to liberalization of electrical energy production. Currently, there is a growing interest for asymptotically stabilizing the grid frequency via model predictive control (MPC). However, the centralized implementation of standard MPC is hampered by the scale and complexity of power networks. In this paper we therefore evaluate the suitability of a scalable, distributed Lyapunov based MPC algorithm as an alternative to conventional balancing techniques. The approach is particularly suited for large scale power networks, as it employs only local information and limited communication between directly-coupled generator buses to provide a stabilizing control action. The effectiveness of the distributed control scheme is assessed by simulating it in closed-loop with the 7-machine CIGRE benchmark system.

Impact of design variables on balancing markets

In electrical power systems there must always exist a balance between generation and load. Any imbalance will result in a frequency deviation and a deviation from the scheduled cross border exchange. To maintain and restore the balance in a certain area balancing systems exist. Balancing systems based on balance responsibility are affected by several design variables. This article discusses the effect of the programme time unit length and the gate-closure time.

Congestion management in the deregulated electricity market: An assessment of locational pricing, redispatch and regulation

We analyze the fundamental differences between locational pricing and redispatch-based congestion management, followed by an assessment of their effects on grid operation and market efficiency. It is indicated that although optimal nodal pricing and congestion redispatch can provide equal results in terms of power injections, they are not equivalent in terms of short-run social welfare. Moreover, a modeling framework is presented to decouple and analyze the effects of transmission system operator/regulator and prosumer behavior on energy market efficiency in a transparent fashion. All results are illustrated on the basis of case studies for the IEEE 39-bus New England test network.