Generator Modeling Practices for Renewable Energy Implementation

Generator Modeling Practices for Renewable Energy Implementation

DOI: 10.4018/978-1-4666-2839-7.ch004

Abstract

In order to integrate a renewable energy resource into the current electrical grid infrastructure, a comprehensive assessment of the demand-side usage patterns is compulsory. It includes such economic variables as tourism within the area, fuel usage, industrial/agricultural output, labor productivity, employment by sector, household income, and the electricity prices by group. A few of the key metrics as inputs for the integrated resource planning are described in this chapter. A thorough assessment and detailed analysis is also required for each generator request to investigate the proposed electrical impact on the grid.
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Overview

The key characteristic for the successful integration of new generation within the electrical grid is planning. The complexities involved with the inter-related components of the power system requires detailed analysis and models to quantify specific attributes in the electric footprint. The models are then evaluated and thoroughly assessed to determine how the system reacts to real-world conditions with various change. Hence, the models are an important asset in the planning process. The system analysis that formulates the basic upgrade of the generations and transmission entails integrated econometric linear-programming based models, which includes statistical analysis, optimization, simulation, and the calculus of variations. The major function of the system models is to provide detailed analysis of a proposed modification and prescribe specific alternative solutions – typically addressed by multi-criteria decision methodologies (Kendall & Kendall, 1994). The optimization process in generation capacity planning (expansion) and a utility integrated resource plan, for example, is a non-linear process, integer, stochastic, and multi-objective optimization problem designed to minimize cost and maximize reliability. The integrated resource planning considers a full-range of feasibility supply-side and electrical demand-side options and evaluates each against a common set of objectives. The paradigm provides an opportunity for system planners to address complex issues in an inclusive, structured, and seemingly transparent method. The techniques utilized tend to be more diagnostic than prognostic in most cases where the health of the system is described but not necessarily how to cure it. Nevertheless, the primary objective of integrated resource planning from an electrical utility perspective is to develop a least-cost alternative (plan) that can meet the customer-energy service requirements and environmental improvements. Planning is an important tenet in the renewable energy generation implementation process. It was espoused by one of the early leaders in America who once said:

By failing to prepare, you are preparing to fail. (Benjamin Franklin ~ 1706 - 1790)

When the selected generator system integration plan is agreed upon by the stakeholders, an intensive evaluation is conducted based on the electrical impact within the proposed footprint. The plan, or request, then enters a queue for deeper analysis of the system, for impact studies typically at the regional Independent Service Operator facility. The major problems encountered at this level are “request queue overload,” where most plans are insufficient financially and deficient in resources. The queue quickly becomes backlogged as higher-priority transmission and distribution (T&D) projects are evaluated over the generation requests. The integrated resource plan requires detailed analysis based on realistic scenarios of the electrical footprint (economic conditions, electricity prices by group, tourism, etc.) as well as the proposed generator parameters. The restructuring of the system impact study firstly lien the input of the acceptable plans to evaluate, and secondly, with the implementation of a “time-phase” analysis system that relieves the burden experienced within the queue itself. Both propositions are cost-effective measures designed to increase request throughput and reduce cycle time.

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