Guillermo Jimenez-Estevez

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.

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.

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.

Fuzzy Arithmetic for the DC Load Flow

The consideration of uncertainties in the future system operation is a key aspect in current planning methodologies. In this context, load flow studies based on probabilistic theory, fuzzy numbers, and Monte Carlo simulations have been proposed in the literature. This work analyzes in a novel way the application of fuzzy number arithmetic for the DC load flow problem. In this sense, fuzzy sets theory is reviewed and is showed that there are two alternative and valid procedures to perform a subtraction. A key aspect for the right selection of one of these procedures is the independency or dependency among the involved variables. Assuming input data as independent variables, this work is focused on analyzing the fuzzy subtraction between dependent state variables of the system, such as voltage phase angle. Accordingly, two new valid alternative methodologies are proposed and applied to case studies. Results are compared to previous related works showing the consistency of the proposed methodologies. Future research will be focused on the consideration of ohmic losses and AC network modeling in the field of expansion planning studies.

Determination of Feeder Areas for the Design of Large Distribution Networks

One of the methods for setting distribution tariffs is benchmarking competition. In Chile, a yardstick competition scheme is applied. This yardstick consists of a “model company” that is designed in order to quantify what would be the least cost of operating an efficient distribution company serving the same demand as the actual company. Input data for the design of the companys network are load locations, magnitudes, and growth forecast, and feasible feeder routes. The design of this network, considering its high combinatorial degree, size, and operational constraints, is a difficult task and it is known to be an NP hard problem. In this paper, a new approach is presented where the problem is separated by feeder areas. The criterion used for dividing the areas is to maintain an equitable distribution of loads among them. Two methodologies are compared for area dimensioning: 1) Voronoi tessellation and 2) k-means clustering. Once the feeder areas are defined, the network is designed by applying a genetic algorithm based on the generation of spanning trees. Finally, in order to fulfill real operational constraints, it is considered that all of the feeders share the same output from the substation. This is achieved by using an algorithm that identifies shared routes and performs the final step of the design of the system.

Heat and Dust: The Solar Energy Challenge in Chile

Chile offers unique conditions for the development of solar energy solutions. The new government is drawing up an ambitious road map for the development of solar energy. Some visions include the positioning of Chile as a regional solar energy exporter. This article examines this trend, based on the development of the power sector, the power market, analysis models, innovation opportunities, and the public policies implemented over the past ten years. It provides an overview of the solar energy scenario for Latin America and examines the technical challenges faced by the various solar technologies, and it discusses transmission systems and storage solutions. The issues of citizen empowerment, community engagement, and sustainable development in the context of solar energy development in the region are discussed along with the need for development of the human capital required to address alternative solar energy challenges from a technical and social standpoint.

Social SCADA and Demand Response for sustainable isolated microgrids

A critical aspect concerning an effective development of isolated microgrids is economical sustainability. Most of the efforts are related to social responsibility of local productive companies that look for avoiding long term commitments with the community. This work proposes a composed solution that combines a social SCADA approach with a Demand Response program. Thus, minimum maintenance costs and a better community involvement for both energy consumption and cost covering can be achieved. A social SCADA solution guarantees the participation of the community through an appropriate communication and information exchange procedure, allowing them to execute tasks associated to supervision and maintenance of the micro grid, and equipment repairing. On the other hand there is a demand response program that relies on a forecast of renewable energy availability as a function of weather predictions (i.e. solar radiation, wind speed). In this context, this forecast is sent to people so they are able to react and adapt their consumption, allowing a better use of the renewable resources and decreasing operational costs (fuel consumption). The demand response program interface consists of a 24 hour clock that assigns a specific color, describing the energy availability, for each hour. This approach is applied to a smart micro grid composed of photovoltaic panels, a wind turbine, a diesel generator, a battery bank, and a water supply system. Results show the effects of community participation through both operational and sustainability indicators, and the economic impact of community participation.

Support vector machines for on-line security analysis of power systems

The pattern recognition approach for security analysis (SA) of power systems has been presented as a promising tool for on-line applications. This paper applies a learning-based nonlinear classifier, which is a support vector machine (SVM) for SA. Three single SVM are trained to classify the state of the system: secure, alert and emergency. The final classification is obtained combining the output of each classifier with a Bayesian rule. The effectiveness of the proposed approach has been demonstrated on two IEEE test systems.