Advanced Control for Energy Management of Grid-Connected Hybrid Power Systems in the Sugar Cane Industry

This work presents a process supervision and advanced control structure, based on Model Predictive Control (MPC) coupled with disturbance estimation techniques and a finite-state machine decision system, responsible for setting energy productions set-points. This control scheme is applied to energy generation optimization in a sugar cane power plant, with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as biomass. The energy plant is bound to produce steam in different pressures, cold water and, imperiously, has to produce and maintain an amount of electric power throughout each month, defined by contract rules with a local distribution network operator (DNO). The proposed predictive control structure uses feedforward compensation of estimated future disturbances, obtained by the Double Exponential Smoothing (DES) method. The control algorithm has the task of performing the management of which energy system to use, maximize the use of the renewable energy sources, manage the use of energy storage units and optimize energy generation due to contract rules, while aiming to maximize economic profits. Through simulation, the proposed system is compared to a MPC structure, with standard techniques, and shows improved behavior.Ministerio de Economía y Competitividad CNPq401126/2014-5Ministerio de Economía y Competitividad CNPq303702/2011-7Ministerio de Economía y Competitividad DPI2016-78338-

Risk-Averse Model Predictive Operation Control of Islanded Microgrids

In this paper we present a risk-averse model predictive control (MPC) scheme for the operation of islanded microgrids with very high share of renewable energy sources. The proposed scheme mitigates the effect of errors in the determination of the probability distribution of renewable infeed and load. This allows to use less complex and less accurate forecasting methods and to formulate low-dimensional scenario-based optimisation problems which are suitable for control applications. Additionally, the designer may trade performance for safety by interpolating between the conventional stochastic and worst-case MPC formulations. The presented risk-averse MPC problem is formulated as a mixed-integer quadratically-constrained quadratic problem and its favourable characteristics are demonstrated in a case study. This includes a sensitivity analysis that illustrates the robustness to load and renewable power prediction errors

Energy management in microgrids with renewable energy sources: A literature review

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed

On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified

Predictive Energy Management of Islanded Microgrids with Photovoltaics and Energy Storage

Islanded microgrids powered primarily by photovoltaic (PV) arrays present a challenging control problem due to the intermittent production and the relatively close scale between the sources and the loads. Energy storage in such microgrids plays an important role in balancing supply with demand, and in extending operation during periods when the PV supply is not available or insufficient. The efficient operation of such microgrids requires effective management of all resources. A predictive energy management strategy can potentially avoid or effectively mitigate upcoming outages. This thesis presents an energy management system (EMS) for such microgrids. The EMS uses a predictive approach to set operational schedules in order to (a) prolong the supply to critical system loads and (2) minimize the chances and duration of system-wide outages, specifically through pre-emptive load shedding. Online weather forecast data has been combined with the PV system model to assess potential energy production over a 48 hour period. These predictions, along with load forecasts and a model of the energy storage system, are used to predict the state-of-charge of the storage devices and characterize potential power shortages. Pre-emptive load shedding is subsequently planned and executed to avert outages or minimize the duration of unavoidable outages. A bounding technique has also been proposed to account for uncertainties in estimates of the stored energy. The EMS has been implemented using an event-driven framework with network communication. The approach has been validated through simulations and experiments using recorded real-world solar irradiance data. The results show that the outage durations have been reduced by a factor of 87% to 100% for an example operating scenario, selected to demonstrate the features of the scheme. The impact of uncertainties in the prediction models has also been investigated, specifically for the PV system rating and the battery capacity. A technique has been developed to compensate for such uncertainties by analyzing the data streams from the source and storage units. The technique is applied to the developed EMS strategy, where it is able to shorten the total outage duration by a factor of 12% over a 42-day scenario exhibiting a variety of irradiance conditions

A Distributed Mixed-Integer Framework to Stochastic Optimal Microgrid Control

This paper deals with distributed control of microgrids composed of storages, generators, renewable energy sources, critical and controllable loads. We consider a stochastic formulation of the optimal control problem associated to the microgrid that appropriately takes into account the unpredictable nature of the power generated by renewables. The resulting problem is a Mixed-Integer Linear Program and is NP-hard and nonconvex. Moreover, the peculiarity of the considered framework is that no central unit can be used to perform the optimization, but rather the units must cooperate with each other by means of neighboring communication. To solve the problem, we resort to a distributed methodology based on a primal decomposition approach. The resulting algorithm is able to compute high-quality feasible solutions to a two-stage stochastic optimization problem, for which we also provide a theoretical upper bound on the constraint violation. Finally, a Monte Carlo numerical computation on a scenario with a large number of devices shows the efficacy of the proposed distributed control approach. The numerical experiments are performed on realistic scenarios obtained from Generative Adversarial Networks trained an open-source historical dataset of the EU