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This article puts forth a multi-objective power development planning model to examine changes in production decisions and carbon dioxide pollution under uncertainty in demand and supply of electricity. The lack of certainty of demand and the coefficient of electricity production capacity (supply uncertainty) were expressed as a fuzzy set, and a multi-objective fuzzy model was used for a case study of Kerman province, Iran power generation capacity expansion planning system for a 12-year period. The results of the study showed that in order to meet demand and to ensure the economic (minimizing production costs) and environmental (minimizing the costs of carbon dioxide pollution) objectives at the same time under the uncertainty of demand and production capacity coefficient, the capacity of Onshore Wind and hydropower technology along with coal fuel should be respectively expanded for about 1000, 938.130, and 163.720 MW. However, as far as only economic goals are concerned, the plan will include the increase of 1127 MW of coal energy production. Furthermore, to meet environmental goals, 1000, 1000, and 500 MW of renewable, wind and hydropower, and photovoltaic technologies are expected in the plan. Finally, the results of the simultaneous provision of economic and environmental objectives regardless of the uncertainties showed the expansion of the capacity of wind, coal, and water technologies to 1000, 542.860, and 140.560 MW, respectively. The difference in results reveals the importance of integrated and comprehensive planning for the expansion of power generation capacity. Therefore, decision makers can continue the sustainable planning of electricity production capacity expansion in terms of uncertainty about the demand side and supply and in the context of a comprehensive economic and environmental framework.

This report assesses the potential of electricity contract auctions as a procurement option for the World Bank's client countries. It focuses on the role of auctions of electricity contracts designed to expand and retain existing generation capacity. It is not meant to be a 'how-to' manual. Rather, it highlights some major issues and options that need to be taken into account when a country considers moving towards competitive electricity procurement through the introduction of electricity auctions. Auctions have played an important role in the effort to match supply and demand. Ever since the 1990s, the use of long-term contract auctions to procure new generation capacity, notably from private sector suppliers, has garnered increased affection from investors, governments, and multilateral agencies in general, as a means to achieve a competitive and transparent procurement process while providing certainty of supply for the medium to long term. However, the liberalization of electricity markets and the move from single-buyer procurement models increased the nature of the challenge facing system planners in their efforts to ensure an adequate and secure supply of electricity in the future at the best price. While auctions as general propositions are a means to match supply with demand in a cost-effective manner, they can also be and have been used to meet a variety of goals.

To solve real life problems under uncertainty in Economics, Finance, Energy, Transportation and Logistics, the use of stochastic optimization is widely accepted and appreciated. However, the nature of stochastic programming leads to a conflict between adaptability to reality and tractability. To formulate a multistage stochastic model, two types of formulations are typically adopted: the so-called stage-scenario formulation named also formulation with explicit non-anticipativity constraints and the so-called nodal formulation named also formulation with implicit non-anticipativity constraints . Both of them have advantages and disadvantages. This work aims at helping the scholars and practitioners to understand the two types of notation and, in particular, to reformulate with the nodal formulation a model that was originally defined with the stage-scenario formulation presenting this implementation in the algebraic language GAMS. In addition, this work presents an empirical analysis applying the two formulations both without any further decomposition to perform a fair comparison. In this way, we show that the difficulties to implement the model with the nodal formulation are somehow reworded making the problem tractable without any decomposition algorithm. Still, we remark that in some other applications the stage-scenario formulation could be more helpful to understand the structure of the problem since it allows to relax the non-anticipativity constraints.

The increasing frequency of the occurrence of extreme weather due to climate change constitutes significant challenges for the stability of existing electricity generation technologies. With more flexible access to multiple generation technologies and various energy resources, both utility operators and energy consumers are keen to embrace mixed power generation alternatives. Though theoretical studies on generation capacity expansion and technology choices have shed light on the applicability of hybrid generations; in reality, the lack of feasibility studies on the practical implementation has been a limitation. For example, the physics, and performance, of these systems are influenced by ambient operational factors such as temperature, humidity, and pressure. This paper provides a more detailed examination of the generation capacity expansion and the technology choice under the existence of temperature changes based on the real-time modeling and simulation of the physical systems using the Dynamic Modeling Laboratory (Dymola) platform. The preliminary simulation results indicate that a threshold of approximately 17°C temperature increase could be identified for the power generation capacity expansion.

This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world's knowledge of physical infrastructure in Africa. The AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a more solid empirical foundation for prioritizing investments and designing policy reforms in the infrastructure sectors in Africa. The book draws upon a number of background papers that were prepared by World Bank staff and consultants, under the auspices of the AICD. The main findings were synthesized in a flagship report titled Africa's infrastructure: A time for transformation, published in November 2009. Meant for policy makers, that report necessarily focused on the high-level conclusions. It attracted widespread media coverage feeding directly into discussions at the 2009 African union commission heads of state summit on infrastructure.

The policy decision of extending electric power transmission lines to connect a remote area to a primary grid vs. developing local electricity generation resources must be informed by studies considering both alternatives’ economic and environmental outcomes. Such analysis must also consider the uncertainty of several factors such as fuel prices, the cost and performance of renewable and conventional power generation technologies, and the value of environmental benefits. This paper presents a method for this analysis, making two main contributions to the literature. First, it shows how to characterize the two alternatives (i.e., main-grid extension vs. local power generation) in detail for precise quantification of their capital and operating costs while guaranteeing that they are both adequate to meet forecast demand and operating reserves. Second, it shows how to properly account for the economic and environmental implications of renewable energy intermittency and uncertainty through the optimization of capital investments and hourly operations. The method is illustrated by applying this analysis method to Saudi Arabia, where the government is struggling to outline a strategy to meet residential and commercial loads reliably and sustainably in the country’s remote, scattered, isolated areas. To meet this demand, the Saudi government is considering two main alternatives: (1) extending the primary power transmission grid; or (2) installing an optimal combination of off-grid distributed generation (DG) resources, including solar PV, wind, diesel, oil, heavy fuel oil, and Li-ion batteries, to generate the electricity locally. Results suggest that under most scenarios of capital costs, fuel prices, and costs of air pollution, developing a microgrid with a large share of wind and solar power is more cost-effective than extending a primary grid 150 km or more away. Extending a primary grid powered by gas-fired combined-cycle power plants is more economical only if the load is not very high, the distance is not more than 350 km, and oil prices are relatively high compared to natural gas.

The problem of power grid capacity expansion focuses on adding or modernizing generation and transmission resources to respond to the rise in demand over a long-term planning period. Traditionally, the problem has been mainly viewed from technical and financial perspectives. However, with the rise in the frequency and severity of natural disasters and their dire impacts on society, it is paramount to consider the problem from a nexus of resilience, sustainability, and equity. This paper presents a novel multi-objective optimization framework to perform power grid capacity planning, while balancing the cost of operation and expansion with the life cycle impacts of various technologies. Further, to ensure equity in grid resilience, a social vulnerability metric is used to weigh the energy not served based on the capabilities (or lack thereof) of communities affected by long-duration power outages. A case study is developed for part of the bulk power system in the state of Colorado. The findings of the study show that, by considering life cycle impacts alongside cost, grid expansion solutions move towards greener alternatives because the benefits of decommissioning fossil-fuel-based generation outweigh the costs associated with deploying new generation resources. Furthermore, an equity-based approach ensures that socially vulnerable populations are less impacted by disaster-induced, long-duration power outages.

Generation expansion planning is critical for the sustainable development of power systems, particularly with the increasing integration of renewable energy sources like wind power. This paper presents an innovative generation expansion model identifying the optimal strategy for constructing new wind power plants. The model determines the ideal size of wind power generation and strategically allocates wind resources across multi-area power systems to maximize their capacity credit. A novel fuzzy set approach addresses wind power’s inherent uncertainty and variability, which models wind data uncertainty through membership functions for each stochastic parameter. This method enhances the accuracy of capacity credit calculations by effectively capturing the unpredictable nature of wind power. The model uses the Effective Load Carrying Capability (ELCC) as the objective function to measure the additional load that can be reliably supported by wind generation. Additionally, integrating a compressed-air energy storage system (CAESS) is introduced as a novel solution to mitigate the intermittency of wind power, further boosting the wind power plants’ capacity credit. By incorporating an energy storage system (ESS), the model ensures greater resource availability and flexibility. The study evaluates a multi-area power network, where each area has distinct conventional generation capacity, reliability metrics, load profiles, and wind data. A three-interconnected power system case study demonstrates the model’s effectiveness in increasing the load carrying capability of intermittent renewable resources, improving system reliability, and enhancing resilience. This study provides new insights into optimizing renewable energy integration by leveraging advanced uncertainty modeling and energy storage, contributing to the long-term sustainability of power systems.

Against the backdrop of an intensifying global climate change and energy crisis, energy system decarbonization constitutes a primary sector for carbon mitigation. Integrated Energy Systems (IES) of district heating systems (DHS), a critical component of district energy networks (DEN), enable energy cascade utilization and enhance renewable energy integration efficiency when coupled with incremental distribution networks (IDN). However, retrofitting coupled systems necessitates significant capital investment and sustained operational expenditures. To evaluate the economic and environmental benefits of system retrofitting and assess cross-sector coordinated optimization potential, this study develops a multi-objective optimization framework for IES transition planning of DHS. Using an operational DHS energy station as a case study, we establish multi-scenario retrofitting strategies and operational protocols with comprehensive feasibility assessments, incorporating sensitivity analysis of cross-sector optimization potential while evaluating how varying electricity-to-heat load ratios affect optimization performance. Results demonstrate that intelligent operation optimization is essential for coordinating multi-equipment operations and maximizing energy conservation. Significant long-term economic and carbon mitigation potential remains untapped in ground source heat pumps and combined cooling, heating, and power (CCHP) systems. Coordinated optimization with campus incremental distribution networks further enhances energy cascade utilization in urban energy systems.

Surface water reservoirs provide us with reliable water supply, hydropower generation, flood control and recreation services. Yet reservoirs also cause flow fragmentation in rivers and lead to flooding of upstream areas, thereby displacing existing land-use activities and ecosystems. Anticipated population growth and development coupled with climate change in many regions of the globe suggests a critical need to assess the potential for future reservoir capacity to help balance rising water demands with long-term water availability. Here, we assess the potential of large-scale reservoirs to provide reliable surface water yields while also considering environmental flows within 235 of the world's largest river basins. Maps of existing cropland and habitat conservation zones are integrated with spatially-explicit population and urbanization projections from the Shared Socioeconomic Pathways to identify regions unsuitable for increasing water supply by exploiting new reservoir storage. Results show that even when maximizing the global reservoir storage to its potential limit (∼4.3-4.8 times the current capacity), firm yields would only increase by about 50% over current levels. However, there exist large disparities across different basins. The majority of river basins in North America are found to gain relatively little firm yield by increasing storage capacity, whereas basins in Southeast Asia display greater potential for expansion as well as proportional gains in firm yield under multiple uncertainties. Parts of Europe, the United States and South America show relatively low reliability of maintaining current firm yields under future climate change, whereas most of Asia and higher latitude regions display comparatively high reliability. Findings from this study highlight the importance of incorporating different factors, including human development, land-use activities, and climate change, over a time span of multiple decades and across a range of different scenarios when quantifying available surface water yields and the potential for reservoir expansion.