Philipp Braun

Distributed and Decentralized Control of Residential Energy Systems Incorporating Battery Storage

The recent rapid uptake of residential solar photovoltaic installations provides many challenges for electricity distribution networks designed for one-way power flow from the generator to residential customers via transmission and distribution networks. For grid-connected installations, large amounts of generation during low load periods or intermittent generation can lead to a difficulty in balancing supply and demand, maintaining voltage and frequency stability, and may even result in outages due to overvoltage conditions tripping protection circuits. In this paper, we present four control methodologies to mitigate these difficulties using small-scale distributed battery storage. These four approaches represent three different control architectures: 1) centralized; 2) decentralized; and 3) distributed control. These approaches are validated and compared using data on load and generation profiles from customers in an Australian electricity distribution network.

Predictive Control Algorithms: Stability despite Shortened Optimization Horizons

Abstract The stability analysis of model predictive control schemes without terminal constraints and/or costs has attracted considerable attention during the last years. We pursue a recently proposed approach which can be used to determine a suitable optimization horizon length for nonlinear control systems governed by ordinary differential equations. In this context, we firstly show how the essential growth assumption involved in this methodology can be derived and demonstrate this technique by means of a numerical example. Secondly, inspired by corresponding results, we develop an algorithm which allows to reduce the required optimization horizon length while maintaining asymptotic stability or a desired performance bound. Last, this basic algorithm is further elaborated in order to enhance its robustness properties.

A Distributed Optimization Algorithm for the Predictive Control of Smart Grids

In this paper, we present a hierarchical, iterative distributed optimization algorithm and show that the algorithm converges to the global solution of a particular optimization problem. The motivation for the distributed optimization problem is the predictive control of a smart grid, in which the states of charge of a network of residential-scale batteries are optimally coordinated so as to minimize variability in the aggregated power supplied to/from the grid by the residential network. The distributed algorithm developed in this paper calls for communication between a central entity and an optimizing agent associated with each battery, but does not require communication between agents. The distributed algorithm is shown to achieve the performance of a large-scale centralized optimization algorithm, but with greatly reduced communication overhead and computational burden. A numerical case study using data from an Australian electricity distribution network is presented to demonstrate the performance of the distributed optimization algorithm.

Predictive control of a Smart Grid: A distributed optimization algorithm with centralized performance properties

The authors recently proposed several model predictive control (MPC) approaches to manage residential level energy generation and storage, including centralized, distributed, and decentralized schemes. As expected, the distributed and decentralized schemes result in a loss of performance but are scalable and more flexible with regards to network topology. In this paper we present a distributed optimization approach which asymptotically recovers the performance of the centralized optimization problem performed in MPC at each time step. Simulations using data from an Australian electricity distribution company, Ausgrid, are provided showing the benefit of a variable step size in the algorithm and the impact of an increasing number of participating residential energy systems. Furthermore, when used in a receding horizon scheme, simulations indicate that terminating the iterative distributed optimization algorithm before convergence does not result in a significant loss of performance.

Towards Dynamic Contract Extension in Supplier Development

We consider supplier development within a supply chain consisting of a single manufacturer and a single supplier. Because investments in supplier development are usually relationship-specific, safeguard mechanisms against the hazards of partner opportunism have to be installed. Here, formal contracts are considered as the primary measure to safeguard investments. However, formal contracts entail certain risks, e.g., a lack of flexibility, particular in an ambiguous environment. We propose a receding horizon control scheme to mitigate possible contractual drawbacks while significantly enhancing the supplier development process and, thus, to increase the overall supply chain profit. Our findings are validated by a numerical case study.

Hierarchical distributed model predictive control of interconnected microgrids

In this work, we propose a hierarchical distributed model predictive control strategy to operate interconnected microgrids (MGs) with the goal of increasing the overall infeed of renewable energy sources. In particular, we investigate how renewable infeed of MGs can be increased by using a transmission network allowing the exchange of energy. To obtain an model predictive control scheme, which is scalable with respect to the number of MGs and preserves their independent structure, we make use of the alternating direction method of multipliers leading to local controllers communicating through a central entity. This entity is in charge of the power lines and ensures that the constraints on the transmission capacities are met. The results are illustrated in a numerical case study.

Towards price-based predictive control of a small scale electricity network

ABSTRACT With the increasing deployment of battery storage devices in residential electricity networks, it is essential that the charging and discharging of these devices be scheduled so as to avoid adverse impacts on the electricity distribution network. In this paper, we propose a non-cooperative, price-based hierarchical distributed optimisation approach that provably recovers the centralised, or cooperative, optimal performance from the point of view of the network operator. The distributed optimisation algorithm provides important insights into the appropriate design of contracts between an energy provider and their associated residential customers, who can themselves act as energy providers as well as consumers (e.g. due to rooftop solar photovoltaics and batteries) depending on the time of the day and on real-time prices. To make the presentation self-contained, and to highlight key properties of the price-based optimisation algorithm, the dual ascent method and its convergence properties are reviewed. The performance of the proposed price-based optimisation algorithm is validated on recent measurement taken from an Australian electricity distribution company, Ausgrid. In addition to analysing the results of the open loop solution, we investigate the effect of real-time prices in the closed loop using a model predictive control framework.

Model Predictive Control of Residential Energy Systems Using Energy Storage and Controllable Loads

Local energy storage and smart energy scheduling can be used to flatten energy profiles with undesirable peaks. Extending a recently developed model to allow controllable loads, we present Centralized and Decentralized Model Predictive Control algorithms to reduce these peaks. Numerical results show that the additional degree of freedom leads to improved performance.