Doris Sáez

A Microgrid Energy Management System Based on the Rolling Horizon Strategy

A novel energy management system (EMS) based on a rolling horizon (RH) strategy for a renewable-based microgrid is proposed. For each decision step, a mixed integer optimization problem based on forecasting models is solved. The EMS provides online set points for each generation unit and signals for consumers based on a demand-side management (DSM) mechanism. The proposed EMS is implemented for a microgrid composed of photovoltaic panels, two wind turbines, a diesel generator and an energy storage system. A coherent forecast information scheme and an economic comparison framework between the RH and the standard unit commitment (UC) are proposed. Solar and wind energy forecasting are based on phenomenological models with updated data. A neural network for two-day-ahead electric consumption forecasting is also designed. The system is tested using real data sets from an existent microgrid in Chile (ESUSCON). The results based on different operation conditions show the economic sense of the proposal. A full practical implementation of the system for ESUSCON is envisioned.

Hybrid adaptive predictive control for the multi-vehicle dynamic pick-up and delivery problem based on genetic algorithms and fuzzy clustering

In this paper, we develop a family of solution algorithms based upon computational intelligence for solving the dynamic multi-vehicle pick-up and delivery problem formulated under a hybrid predictive adaptive control scheme. The scheme considers future demand and prediction of expected waiting and travel times experienced by customers. In addition, this work includes an analytical formulation of the proposed prediction models that allow us to search over a reduced feasible space. Predictive models consider relevant state space variables as vehicle load and departure time at stops. A generic expression of the system cost function is used to measure the benefits in dispatching decisions of the proposed scheme when solving for more than two-step ahead under unknown demand. The demand prediction is based on a systematic fuzzy clustering methodology, resulting in appropriate call probabilities for uncertain future. As the dynamic multi-vehicle routing problem considered is NP-hard, we propose the use of genetic algorithms (GA) that provide near-optimal solutions for the three, two and one-step ahead problems. Promising results in terms of computation time and accuracy are presented through a simulated numerical example that includes the analysis of the proposed fuzzy clustering, and the comparison of myopic and new predictive approaches solved with GA.

Energy management system for a renewable based microgrid with a demand side management mechanism

A novel energy management system for a renewable based microgrid is proposed. It provides on-line set points for each generation unit, operation modes for a water supply system, and signals for consumers based on a demand side management mechanism. The smart microgrid is composed of photovoltaic panels, a wind turbine, a diesel generator, a battery bank, and a water supply system. The energy management system (EMS) minimizes the operational costs while supplying the water and electric load demands. It considers a two days ahead prediction of the weather conditions. Also, a neural network for a two days ahead electric consumption forecasting is designed. The system is implemented and tested using a real data set from a reference location. Results show the economic sense of the set points and management, for a practical implementation of the system in a specific location in Chile.

Fuzzy predictive control of a solar power plant

This work presents the application of fuzzy predictive control to a solar power plant. The proposed predictive controller uses fuzzy characterization of goals and constraints, based on the fuzzy optimization framework for multi-objective satisfaction problems. This approach enhances model based predictive control (MBPC) allowing the specification of more complex requirements. A brief description of the solar power plant and its simulator is given. Basic concepts of predictive control and fuzzy predictive control are introduced. Two fuzzy predictive controllers using different membership functions are designed for a solar power plant, and they are compared with a classical predictive controller. The simulation results show that the fuzzy MBPC formulation, based on a well proven successful algorithm, gives a greater flexibility to characterize the goals and constraints than classical control.

Secondary Control Strategies for Frequency Restoration in Islanded Microgrids With Consideration of Communication Delays

One of the well-known methods to share active and reactive power in microgrids (MGs) is droop control. A disadvantage of this method is that in steady state the frequency of the MG deviates from the nominal value and has to be restored using a secondary control system (SCS). The signal obtained at the output of the SCS is transmitted using a communication channel to the generation sources in the MG, correcting the frequency. However, communication channels are prone to time delays, which should be considered in the design of the SCS; otherwise, the operation of the MG could be compromised. In this paper, two new SCSs control schemes are discussed to deal with this issue: (1) a model predictive controller (MPC); and (2) a Smith predictor-based controller. The performance of both control methodologies are compared with that obtained using a conventional proportional integral-based SCS using simulation work. Stability analysis based on small signal models and participation factors is also realized. It is concluded that in terms of robustness, the MPC has better performance.

Hybrid predictive control strategy for a public transport system with uncertain demand

In this article, a hybrid predictive control (HPC) strategy is formulated for the real-time optimisation of a public transport system operation run using buses. For this problem, the hybrid predictive controller corresponds to the bus dispatcher, who dynamically provides the optimal control actions to the bus system to minimise users’ total travel time (on-vehicle ride time plus waiting time at stops). The HPC framework includes a dynamic objective function and a predictive model of the bus system, written in discrete time, where events are triggered when a bus arrives at a bus stop. Upon these events, the HPC controller makes decisions based on two well-known real-time transit control actions, holding and expressing. Additionally, the uncertain passenger demand is included in the model as a disturbance and then predicted based on both offline and online information of passenger behaviour. The resulting optimisation problem of the HPC strategy at every event is Np-hard and needs an efficient algorithm to solve it in terms of computation time and accuracy. We chose an ad hoc implementation of a Genetic Algorithm that permits the proper management of the trade-off between these two aspects. For real-time implementation, the design of this HPC strategy considers newly available transport technology such as the availability of automatic passenger counters (APCs) and automatic vehicle location (AVL) devices. Illustrative simulations at 2, 5 and 10 steps ahead are conducted, and promising results showing the advantages of the real-time control schemes are reported and discussed.

Identification of Critical Spans for Monitoring Systems in Dynamic Thermal Rating

Dynamic thermal rating (DTR) has been seen as an important tool for planning and operation of power systems, and recently, for smart-grid applications. To implement an effective DTR system, it is necessary to install monitoring stations along the studied lines, with a tradeoff between accurate estimations and equipment investments. In this paper, a novel heuristic is developed for identifying the number and locations of critical monitoring spans for the implementation of DTR. The heuristic is based on the use of historical-simulated weather data, obtained from a Mesoscale Weather Model, and the statistical analysis of the thermal capacities computed in each span along the line. The heuristic is applied to a line that is 325 km long in North Chile. Optimal monitoring sets, including the number and location of required monitoring stations, are determined for different confidence levels in all line segments. The results are compared to an equidistant monitoring strategy. The proposed heuristic shows robustness since it outperforms the equidistant monitoring strategy in all of the analyzed cases, especially for the longer line segments, which are subject to more complex weather patterns.

Fuzzy Predictive Supervisory Control Based on Genetic Algorithms for Gas Turbines of Combined Cycle Power Plants

This work presents a novel design and development of a fuzzy predictive supervisory controller, based on genetic algorithms (GA), for gas turbines of combined cycle units. The control design is based on an objective function that represents the economic and regulatory performance of a gas turbine by using a dynamic optimal set-point for the regulatory level. A fuzzy model is considered in order to characterize the nonlinear behavior of the gas turbine, which is used in two supervisory control systems. The first fuzzy supervisory control design includes a fuzzy model, where its parameters are held constant for the successive predictions. For the second fuzzy supervisory control design, its parameters are updated in each prediction and its nonlinear optimization problem is solved using GAs. The proposed fuzzy supervisory controllers are compared against a supervisory controller based on linear models and a regulatory controller with constant optimal set-points. Results indicate that the fuzzy GA predictive supervisory controller captures adequately the nonlinearities of the process, which, in turn, provides a promising approach to improve the performance of the combined cycle unit.

Load profile generator and load forecasting for a renewable based microgrid using Self Organizing Maps and neural networks

In this paper, two methods for generating the daily load profile and forecasting in isolated small communities are proposed. In these communities, the energy supply is difficult to predict because it is not always available, is limited according to some schedules and is highly dependent on the consumption behavior of each community member. The first method is proposed to be used before the implementation of the microgrid in the design state, and it includes a household classifier based on a Self Organizing Map (SOM) that provides load patterns by the use of the socio-economic characteristics of the community obtained in a survey. The second method is used after the implementation of the microgrid, in the operation state, and consists of a neural network with on-line learning for the load forecasting. The neural network model is trained with real-data of load and it is designed to stay adapted according to the availability of measured data. Both proposals are tested in a real-life microgrid located in Huatacondo, in northern Chile (project ESUSCON). The results show that the estimated daily load profile of the community can be very well approximated with the SOM classifier. On the other hand, the neural network can forecast the load of the community reasonably well two-days ahead. Both proposals are currently being used in a key module of the energy management system (EMS) in the real microgrid to optimize the real uninterrupted load for 24-hour energy supply service.

Fuzzy-model-based hybrid predictive control

In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state-space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs). An illustrative experiment on a hybrid tank system is conducted to demonstrate the benefits of the proposed approach.