A Study of Japan's Energy Landscape in the Transition to Renewable Electricity

Received: 1 February 2024. Revised: 6 March 2024. Accepted: 19 March 2024. Available online: 31 March 2024.The global shift towards renewable energy sources is driven by the desire for a sustainable energy future. Integrating intermittent renewable sources and maintaining grid stability are obstacles that must be overcome to achieve this goal, which is why grid stability and energy storage systems are being investigated in this study using Energy PLAN simulation. This study conducts a thorough analysis of energy storage solutions necessary to support Japan's energy landscape shift to renewable electricity. It offers a comprehensive analysis considering technological, environmental, and policy aspects to evaluate the applicability, difficulties, and potential of renewable electricity. Technical factors emphasize how critical it is to maintain grid balance and consider scalability and technology compatibility with Japan's distinct energy infrastructure. Economic analyses examine revenue streams, levelized storage prices, investment needs, and cost-benefit evaluations to shed light on the viability and appeal of technologies for storing energy from an economic standpoint. The goal of synthesizing these several characteristics is to provide policymakers, and energy stakeholders participating in Japan's ambitious journey towards renewable electricity with strategic insights, practical recommendations, and a roadmap. This study aims to steer Japan's energy landscape towards resilience, sustainability, and diversification by establishing links between imperatives, economic feasibility, and technical breakthroughs

Evaluación mediante indicadores clave de rendimiento del despacho económico hidrotérmico resuelto por medio de técnicas heurísticas

El presente artículo resuelve el problema de coordinación hidrotérmica mediante la utilización de técnicas heurísticas como alternativa a los métodos de optimización exactos. Las técnicas utilizadas son el método de enjambre de partículas, algoritmos genéticos con sus variantes de selección por ruleta y torneo y el novedoso y reciente algoritmo lobo gris. El objetivo del modelo de optimización planteado radica en la minimización de la función objetivo referente al costo de combustibles de las centrales térmicas considerando, además, el efecto de punto de válvula que le da un toque más realista al problema. La metodología de solución propuesta incluye penalizaciones en la función objetivo relacionadas a las violaciones de las restricciones de balances de potencia y el balance dinámico de los reservorios. El despacho económico se ejecuta para un sistema de prueba compuesto por múltiples centrales térmicas y varias centrales hidroeléctricas con reservorios en cascada. Los algoritmos desarrollados se implementaron en el software Matlab. Adicionalmente, se evalúa innovadoramente los resultados logrados por cada técnica heurística mediante la utilización de diferentes indicadores clave de rendimiento.This paper solves the hydrothermal scheduling problem by using heuristic techniques as an alternative to exact optimization methods. The techniques used are the particle swarm method, genetic algorithms with its variants of selection by roulette and tournament and the novel and recent grey wolf algorithm. The objective of the proposed optimization model lies in the minimization of the objective function regarding fuel costs of thermal power plants, also considering the valve point effect that gives a more realistic touch to the problem. The proposed solution methodology includes penalties in the objective function related to the violations of constraints of the power balance and dynamic balance of reservoirs. The economic dispatch is executed for a test system composed of multiple thermal power plants and several hydroelectric power plants with cascaded reservoirs. The algorithms developed were implemented in the Matlab software. Additionally, the results achieved by each heuristic technique are innovatively evaluated using different key performance indicators