Rodrigo Palma-Behnke

Trends in Microgrid Control

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

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.

A distribution company energy acquisition market model with integration of distributed generation and load curtailment options

This work presents a novel day-ahead energy acquisition model for a distribution company (DisCo) in a competitive market based on Pool and financial bilateral contracts. The market structure encompasses wholesale generation companies, distributed generation (DG) units of independent producers, DG units owned by the DisCo, and load curtailment options. Thus, while satisfying its technical constraints, the DisCo purchases active and reactive power according to the offers of DG units, customers, and the wholesale market. The resulting optimal power flow model is implemented with an object-oriented approach, which is solved numerically by making use of a branch and border sequential quadratic programming algorithm. The model is validated in test systems and then applied to a real case study. Results show the general applicability of the proposed model, with potential cost savings for the DisCo. Finally, the analysis of Lagrange multipliers gives valuable information, which can be used to improve the market design and to extend the use of the model to a more general market structure such as a power exchange.

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.

Justified Fault-Ride-Through Requirements for Wind Turbines in Power Systems

In this paper, a novel adaptive strategy to obtain technically justified fault-ride-through requirements for wind turbines (WTs) is proposed. The main objective is to promote an effective integration of wind turbines into power systems with still low penetration levels of wind power based on technical and economical considerations. The level of requirement imposed by the strategy is increased stepwise over time, depending on system characteristics and on wind power penetration level. The idea behind is to introduce stringent requirements only when they are technically needed for a reliable and secure power system operation. Voltage stability support and fault-ride-through requirements are considered in the strategy. Simulations are based on the Chilean transmission network, a midsize isolated power system with still low penetration levels of wind power. Simulations include fixed speed induction generators and doubly fed induction generators. The effects on power system stability of the wind power injections, integrated into the network by adopting the adaptive strategy, are compared with the effects that have the same installed capacity of wind power but only considering WTs able to fulfill stringent requirements (fault-ride-through capability and support voltage stability). Based on simulations and international experience, technically justified requirements for the Chilean case are proposed.

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.

OPF with SVC and UPFC modeling for longitudinal systems

This paper presents a formulation of the Optimal Power Flow problem with an explicit modeling of Static Var Compensator (SVC) and Unified Power Flow Controller (UPFC) devices. The optimization problem is solved by using Sequential Quadratic Programming, where two convergence criteria and four different methods are studied to solve the quadratic subproblems. The proposed model is integrated in an object-oriented based decision support platform for competitive power markets. Validation of the method and practical applications to real longitudinal systems are discussed, where FACTS location and a UPFC-based interconnection are described. Results show the impact of SVC and UPFC FACTS technologies in the physical and economic behavior of a real system.

A Competitive Market Integration Model for Distributed Generation

High penetration of distributed generation (DG) resources is increasingly observed worldwide. The evolution of this process in each country highly depends on the cost of traditional technologies, market design, and promotion programs and subsidies. Nevertheless, as this trend accelerates, higher levels of penetration will be achieved and, in turn, a competitive market integration of DG will be needed for an adequate development of the power sector. This paper proposes a competitive market integration mechanism for DG in a pool-based system. The mechanism encompasses both energy and capacity payment procedures in the wholesale market with DG units located at the distribution level. The proposed model is validated for the current Chilean regulation framework and extended to more general market structures. The model can be considered a novel development on the design of competitive markets for DG resources, which are still dominated by subsidies/compensation schemes.

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.

Smart Microgrids as a Solution for Rural Electrification: Ensuring Long-Term Sustainability Through Cadastre and Business Models

The provision of energy at the local level by using renewable and local resources is increasingly acknowledged as a techno-economic solution for rural electrification. This work describes an approach for implementing microgrid projects at the institutional level by means of a specific entity that uses methods that engage the community in microgrid operation and maintenance (O&M), which ensures long-run benefits. The first step, related to macro-level barriers, is addressed by building a complete cadastre of isolated communities, while the second, at the micro level, focuses on business models for covering investment and O&M costs. A cadastre uncovers the key characteristics of each location (energy resources, availability, socio-economic conditions, environment, etc.). A cadastre also helps identify local needs, develop monitoring strategies, and determine benchmarks among microgrids. Its information also assists with proposing new projects, securing funding, and monitoring actual microgrids. At the micro level, local stakeholders, economic capabilities, social capital, and organizational structures are identified, which contribute to the selection of a tailored business model that can enable fundraising and O&M activities. The approach is presented in a four-stage framework: 1) background data collection; 2) community profile building; 3) system design; and 4) detailed engineering. Each community is evaluated by a prioritization index that considers the electrical conditions of each residence.