• Energy Research

Modern district heating technologies have a great potential to make the energy sector more flexible and sustainable due to their capabilities to use energy sources of varied nature and to efficiently store energy for subsequent use. Central control tasks within these systems for the efficient and safe distribution of heat refer to the stabilization of overall system temperatures and the regulation of storage units state of charge. These are challenging goals when the networked and nonlinear nature of district heating system models is taken into consideration. In this letter, for district heating systems with multiple, distributed heat producers, we propose a decentralized control scheme to provably meet said tasks stably.

Flow and storage volume regulation is essential for the adequate transport and management of energy resources in district heating systems. In this letter, we propose a novel and suitably tailored -- decentralized -- adaptive control scheme addressing this problem whilst offering closed-loop stability guarantees. We focus on a system configuration comprising multiple heat producers, consumers and storage tanks exchanging energy through a common distribution network, which are features of modern and prospective district heating installations. {\color{black}The proposed controller is based on passivity, backstepping and (indirect) adaptive control theory.

This paper aims to achieve a balance of power in a group of prosumers, based on a price mechanism, i.e., to steer the difference between the total production and consumption of power to zero. We first set the information network topology such that the prosumers exchange price (power) information with their neighbors according to a chosen information network topology. Based on the exchanged information and the prosumers own measured power demand, each prosumer uses a local control strategy to turn on and off its power generator to cooperatively achieve the global balance. More specifically, the local control strategy results from a distributed model predictive control method based on dual decomposition and sub-gradient iterations. The method achieves a unique dynamic price signal for each prosumer. Simulation results with realistic data validate the method.

In this article, we consider a dc microgrid composed of distributed generation units (DGUs) trading energy among each other, where the energy price depends on the total current generated by all the DGUs. We then use a Cournot aggregative game to describe the self-interested interaction among the DGUs, where each DGU aims at minimizing the deviation with respect to the given reference signals and maximizing the revenue from the sale of the generated power. Thus, we design a fully distributed continuous-time equilibrium-seeking algorithm to compute the generalized Nash equilibrium (GNE) of the game. We interconnect the designed decision-making algorithm with the dynamics of the microgrid in a passive way, and, by leveraging passivity theory, we prove the convergence of the closed-loop system trajectory to a feasible operating point that is also a Nash equilibrium of the collective aggregative game. Finally, we present extensive simulation results that validate the proposed distributed optimal control scheme, showing excellent performance. IEEE Transactions on Control Systems Technology, 32 (6) ISSN:1063-6536 ISSN:1558-0865

This paper describes the design of a self-scheduled current controller for doubly-fed induction generators in wind energy conversion systems (WECS). The design is based on viewing the mechanical angular speed as an uncertain yet online measurable parameter and on subsuming the problem into the framework of linear parameter-varying (LPV) controller synthesis. An LPV controller is then synthesized to guarantee a bound on the worst-case energy gain for all admissible trajectories of rotor speed in the operating range. Furthermore, this study investigates the robust performance of the LPV controller with respect to other bounded machine parameter variations and the impact of the stator voltage dips on the robustness of the control system. Two closed loop simulation models, one with a conventional control scheme and the other with an LPV control scheme, are developed for the control of the electrical torque and the power factor on the rotor side in order to compare the performance of the control systems. Some simulation results are given to demonstrate the performance and robustness of the control algorithm.

In this article, we present some new results for the design of PID passivity-based controllers (PBCs) for the regulation of port-Hamiltonian (pH) systems. The main contributions of this article are: (i) new algebraic conditions for the explicit solution of the partial differential equation required in this design; (ii) revealing the deleterious impact of the dissipation obstacle that limits the application of the standard PID-PBC to systems without pervasive dissipation; (iii) the proposal of a new PID-PBC which is generated by two passive outputs, one with relative degree zero and the other with relative degree one. The first output ensures that the PID-PBC is not hindered by the dissipation obstacle, while the relative degree of the second passive output allows the inclusion of a derivative term. Making the procedure more constructive and removing the requirement on the dissipation significantly extends the realm of application of PID-PBC. Moreover, allowing the possibility of adding a derivative term to the control, enhances its transient performance.

In this paper we study the optimal control of a micro grid of biogas producers. The paper considers the possibility to have a local storage device for each producer, who partly consumes his own production, i.e. prosumer. In addition, connected prosumers can sell stored gas to create revenue from it. An optimization model is employed to derive the size of storage device and to provide a pricing mechanism in an effort to value the stored gas. Taking into account physical grid constraints, the model is constructed in a centralized scheme of model predictive control. Case studies show that there is a relation between the demand and price profiles in terms of peaks and lows. The price profiles generally follow each other. The case studies are employed as well to to study the impacts of model parameters on deriving the storage size.