http://item.bettergrids.org/handle/1001/99 Fri, 01 May 2026 12:54:23 GMT 2026-05-01T12:54:23Z http://item.bettergrids.org/handle/1001/761 Title: Electricity Market Integration in the GCC and MENA Abstract: We developed the KAPSARC Energy Model (KEM) for Saudi Arabia to understand the dynamics of the country’s energy system. It is a partial equilibrium model formulated as a mixed complementarity problem to capture the administered prices that permeate the local economy. KEM has been previously used to study the impacts of various industrial fuel pricing policies, improved residential energy efficiency on the energy economy, the feasibility of installing coalfired power plants in Saudi Arabia, and reforming residential electricity tariffs. In the present paper, we use it to assess the effects of introducing an optimal power flow formulation for electricity transmission on policy-relevant metrics. Summary The purpose of this study is to assess policy-relevant effects of incorporating a more proper representation of electricity transmission in multi-sector national policy models. This goal is achieved by employing the KAPSARC Energy Model (KEM), which is the first publicly available large-scale energy policy model for Saudi Arabia. Past studies using KEM have examined industrial pricing policy, residential energy efficiency, the prospects of power generation technologies and residential electricity pricing. These studies have shown that under certain fuel pricing scenarios, significant renewable energy capacity is deployed. Previous versions of KEM used a transshipment formulation for electricity transfer, which basically treats it similar to fuel transport. Electricity transmission formulations, however, represent the physical constraints that govern power flows in reallife. The variability and intermittency of renewable power imposes limitations on the operations of the grid and models that do not incorporate a representation of electricity transmission may miss key insights, particularly when large-scale deployment of renewable technologies is contemplated. This study illustrates the methodology and consequences of moving from a transshipment formulation of KEM to one which includes transmission with a single or multiple nodes within each region. Our results show: The optimal investment in photovoltaics (PV) and the marginal costs of delivering electricity change considerably when transmission of electricity both within regions and between regions is incorporated into the model compared to the simple transshipment formulation. The number of nodes in each region described by the model alters the model outcomes more than whether the model incorporates transmission losses or not. However, a version of KEM with a single node in each region for transmission and without accounting for transmission losses still provides valuable insight compared with the transshipment formulation, while keeping the model size tractable Introducing transmission into the model gives results that are more affected by the operations of the power system than by the fuel and technology mix. The market-clearing price of natural gas in a deregulated environment only changes slightly because reduced PV deployment (compared to the transshipment version) is mostly offset by a minor increase in dispatch of gas-fired generation. In a regulatory model where location marginal prices are passed on to consumers, the new model captures the changes in the marginal costs of electricity delivery at the transmission nodes whereas the simpler transshipment formulation would miss this insight. In other words, the transmission component is needed for planning a system where location marginal prices are passed on to consumers. Wed, 01 Jan 2025 00:00:00 GMT http://item.bettergrids.org/handle/1001/761 2025-01-01T00:00:00Z http://item.bettergrids.org/handle/1001/760 Title: Liberia electricity transmission network 2023 Abstract: This dataset gives a full overview of the current (up to 2022) transmission grid infrastructure of Liberia including power plants, power stations, power towers and power lines with attributes such as length, assumed voltage level etc.. The dataset was produced by using smart tracing algorithms developed by NEO in house which uses grid probability map for determining areas to look for power towers and a deep learning model for power tower automatic detection. The power plants and (sub)stations collected from open source (Global power plant database, Global dam dataset, OpenStreetMap and Energydata.info etc.) as well as some existing power towers from OpenStreetMap dataset were used as starting point for smart tracing alogirthm, and Mapbox 50cm Very High Resolution imagery was used as input for detecting power towers using the trained deep learning model. The OSM points are also included into the dataset to make best use of existing dataset to achieve complete grid mapping coverage as much as possible in an efficient and effective way. The probability map and path finder is adapted based on Global Grid Finder approach :https://gridfinder.org/ Global power plant database: https://datasets.wri.org/dataset/globalpowerplantdatabase Global dam dataset: http://globaldamwatch.org/data/ Sun, 01 Jan 2023 00:00:00 GMT http://item.bettergrids.org/handle/1001/760 2023-01-01T00:00:00Z http://item.bettergrids.org/handle/1001/759 Title: South Africa - Global Electrification Platform (GEP) Abstract: [The Global Electrification Platform (GEP)](https://electrifynow.energydata.info) is a multi-phase project led by the World Bank to standardize and simplify the use of geospatial tools for least-cost electrification planning. The GEP provides a high-level overview of the technology mix (grid and off-grid) required to achieve universal access by 2030. It focuses on the countries with access rates below 90 percent and the 50 countries with the highest population deficit, with an intermediated investment prospectus for 2025. The results of the model indicate the least-cost investment requirements based on publicly available information on demand and existing infrastructure. Wed, 01 Jan 2020 00:00:00 GMT http://item.bettergrids.org/handle/1001/759 2020-01-01T00:00:00Z http://item.bettergrids.org/handle/1001/758 Title: Annual 2016 Electric Generator Report Abstract: Annual U.S. generator level data about generators at electric power plants owned and operated by electric utilities and nonutilities (including independent power producers, combined heat and power producers, and other industrials). Based on EIA Form-860 data. Data contained in a zip file. The zip file contains generator-specific information such as initial date of commercial operation, prime movers, generating capacity, energy sources, status of existing and proposed generators, proposed changes to existing generators, county and State location (including power plant address), ownership, and FERC qualifying facility status. The file also includes data on the ability to use multiple fuels; specifically, data on co-firing and fuel switching are included. Sun, 01 Jan 2017 00:00:00 GMT http://item.bettergrids.org/handle/1001/758 2017-01-01T00:00:00Z