Decarbonising municipal solid waste (MSW) is a defining algorithmic challenge for smart cities: waste-to-energy (WtE), composting and material-recovery investments must be committed years ahead under deep uncertainty about household source-separation uptake and the governing policy instruments (landfill taxes, carbon prices, compost subsidies). We present a two-stage stochastic mixed-integer linear programming (MILP) framework, applied to the Attica region of Greece (Athens; 5122 t/day MSW) and calibrated to confirmed 2024 weighbridge data. The contribution is an integration strategy rather than a new technique: endogenous WtE capacity sizing (Special Ordered Sets of Type 2 (SOS2) piecewise-linear cost, economies-of-scale exponent 0.85), the bilinear capacity–build coupling linearised exactly by McCormick envelopes (one factor being binary), and Pigouvian shadow-price recovery of the optimal policy instruments are combined in a single-shot, gap-bounded MILP and embedded in a 10,000-run Latin-hypercube Monte Carlo layer over 12 parameters with Spearman sensitivity indices. The individual components are established; their joint formulation is, to our knowledge, new. The pipeline solves 10,415 MILP instances. Three results are policy-relevant: investment is robust to rollout uncertainty (VSS ≈ €0; EVPI ≈ €5.4 M, 0.15%); the carbon price alone explains ~80% of cost variance (ρ = +0.891); and, under the model’s calibration, the implied Pigouvian-optimal landfill tax (€1100–3300/t) indicates a binding landfill cap is needed to secure diversion. The framework transfers to any metropolitan MSW system facing decarbonisation and circular-economy mandates.

V2G (Vehicle to Grid) is a concept that aims to utilize the capacity of vehicle batteries when vehicles (primarily cars) are stationary. It effectively gives rise to a bi-directional flow of energy between vehicles and the "grid" and "sees" them either as "storage" or as a (virtual) source of energy in order to provide services related to e.g. ancillary services-frequency regulation. Such a concept aims to make better use of network resources and provide a homogeneous flow and utilization of energy among stakeholders e.g. producers, consumers etc. In this paper we will sketch a future scenario of a mass deployment of V2G-enabled vehicles and investigate on a system-level basis the "mobility" dimension present in mobile communication networks.

In attempt to address the power sector issues, provide energy necessary to attain its developmental goals and meet its international commitments, Cameroon has developed several policies and masterplans. However, these policy directives haven't had the desired effect with the issues persisting and the nation falling short of intended milestones. This research analyses the implications of stated and clean energy policies on the future electricity generation system of Cameroon. The study uses the Schwartz's methodology for scenario development and the Low Emissions Analysis Platform (LEAP) to model the reference scenario and three alternative scenarios that describe various policy directives. These scenarios are assessed based on total installed capacity, economic competitiveness, and associated environmental benefits. The results indicate Cameroon's generation capacity in 2045 would need to grow by over 800% under the Reference scenario. This growth would be at a cumulative cost of $3377 million and associated greenhouse gas emissions of 82.6 MTCO2e. Furthermore, only 13.11% renewable energy target would be achieved. The study also shows that higher renewable energy targets result in significant economic and emission savings compared to the Reference scenario. Therefore, Cameroon should reassess its power sector masterplans and intensify efforts to increase uptake of renewables.

This study assesses Cameroon's future energy demand, associated greenhouse gas (GHG) emissions and the impact of various low-carbon transition policies on the energy system from 2016 to 2045. The Low Emissions Analysis Platform (LEAP) model was used to develop the Reference (REF scenario) and three alternative scenarios: the Medium Transition Scenario (MTS), High Transition Scenario (HTS) and Advanced Transition Scenario (ATS). The alternative scenarios define varying ambitions in implementing energy efficiency, demand side management and fuel switching policies on the energy demand sector in the country for socioeconomic development. Results for the REF scenario shows the energy demand and associated GHG emissions increase to 9079.98 ktoe and 10.2 Mt CO2e by 2045, representing annual growth rates of 3.53 % and 2.69 % respectively. The alternative scenarios all experience varying and lower growth rates compared to the REF scenario, the least under the ATS. This indicates switching to cleaner fuels in the residential and transport sectors hold the highest potential for emission savings. The study further recommends key energy efficiency and demand side management measures, financial incentives and other strategies the government could adopt to achieves significant energy emissions reduction.

The Cameroon electricity sector is currently plagued by inadequate generation capacity. The electricity supply deficit has had dire consequences on attaining the country's industrialization, socioeconomic and developmental objectives enshrined in her Vision 2035. However, there are sustainable solutions that minimizes the required new capacity planned by the government. This paper analyses the implications of these sustainable policies on the electricity generation system of Cameroon. An adaptation of the Schwartz's methodology and the Low Emissions Analysis Platform (LEAP), integrated with the Next Energy Modelling system for Optimization (NEMO) was used to develop least-costs scenarios of Cameroon from 2016 to 2045. These scenarios are based on the key factors that would influence the electricity supply/demand of Cameroon. In addition to the business-as-usual scenario which defines the least-cost pathway with no policy interventions, the three alternative scenario categories are: power losses, demand side energy efficiency measures and carbon dioxide emission targets. Results show the alternative scenarios have significant benefits in avoided installed capacity, financial savings and avoided greenhouse gas emissions. This is emphasized by capacity savings of up to 44%, translating to possible economic benefits of up to 25% depending on government's commitment to reducing power losses. Furthermore, potential for emission trading is limited, although it becomes more appealing under a zero-carbon emission transition target in the electricity system. Therefore, the state should prioritize the reduction of power losses and enacting energy efficiency polices if it aims to safely and reliably minimize the energy needed for its economic growth.

In this article, we provide a structured review of crude oil price dynamics. Specifically, we summarize evidence on important factors determining oil prices, cover the impact of oil market shocks on the macro economy and the stock market, discuss how the financialization of crude oil markets affects oil market functionality and efficiency, and we then outline approaches for forecasting crude oil prices and volatility. By comparing the results of the most influential early contributions and recent studies, we can identify important developments and research gaps in each field. Thus, our review provides academics and practitioners newly engaging in crude oil research with an overview of what scientists know about crude oil dynamics and highlights which topics are particularly promising for future research.

Renewable Energy Sources (RES) generation forecasting is an approach to handle the stochasticity of RES. This concept is very crucial to transform RES plants into dispatchable and integrated them for contemporary energy markets. The majority of the literature focuses on individual plants. The data are collected in a site and used as inputs in the forecasting model. The present paper is centered on...

The abundance of renewable energy in Ghana can play an important role in the rural electrification program rolled out by the Government of Ghana to promote energy that everyone can assess for improved living standards. It has the potential to meet the objectives of the energy sector which include; 10% renewable energy in the total generation mix, minimize the adverse effects of energy production on the environment, reduce poverty, and improve the socio-economic development of the country, mainly, in rural communities, creating community-based employment, etc. In this study, Nkrankrom was selected as a case study to evaluate the possibility of meeting their energy needs with a solar mini-grid. To determine the energy demand of the community, a structured questionnaire was used to collect data. The HOMER software was used to perform the financial viability of utilizing the solar resource available. The result shows that it is possible to meet the energy demand of the community from the solar resource available. The proposed system configuration included PV/Battery/Converter with a Levelized Cost of Energy of $0.107/kWh compared to $0.124/kWh (using $1 = GH₵4.98 rate), if the area is to be connected to the national grid. The breakeven distance or Electric Distance Limit (EDL) between standalone mini-grid and grid extension in this analysis was found to be 1.11 km. The study also revealed that solar mini-grid could have an immense benefit to the community both economically and socially such as improve the standard of living as well as meeting the rural development objectives of Ghana.

Market concentration is a sensitive and contradicting issue in power and natural gas markets. This issue concerns the whole European competition policy, as it concerns liberalization process, considers assets with state aid support schemes and covers antitrust and mergers cases. Market concentration is usually tackled under Competition Authorities, through ex-post and ad-hoc evaluation of each case. This creates an uncertainty on whether a market participant is considered to have dominant position in a market, as well as on what and when is allowed for a participant to do concerning its bidding and tariffs formation strategies. The Directorate General for Competition in Europe considers that "if a company has a market share of less than 40%, it is unlikely to be dominant", however there is not specific threshold which identifies a dominant position. On the other hand, National Regulatory Authorities for Energy are responsible for the regulation and market monitoring of power and natural gas markets, however they do not tackle market concentration issues with a coherent and permanent methodology. This chapter describes an ex-ante Market Monitoring and Regulation Mechanism for Market Concentration in Power and Natural Gas Markets. The mechanism concerns the available capacity in both supply and demand sides. In the supply side, it estimates the available capacity of all market participants, considering the capacity per resource type (i.e., lignite, natural gas, large hydro, renewables), market participation type (i.e., FiT, FiP, commissioning), interconnection (entry) point type and existence of bilateral contracts. It imposes a common threshold, i.e., 40% or 50%, for both aggregate and disaggregate markets, namely capacity of each resource/entry point type. In the demand side, it considers load, storage and pumping as well as export interconnections. However, regulation can exclude resource/entry point types with low capacity under another threshold, i.e., disaggregate market up to 2%/2.5% or 4%/5% of the aggregate market, depending on the values of the second threshold. The mechanism also considers the relative size of market participants in the supply and demand side, implementing a fourth threshold, i.e., at 1% or 2/2.5%, depending on the value of the third threshold. The mechanism is a coherent and permanent mechanism. The mechanism provides indicative results concerning the Hellenic power market. It can assist Energy Regulators to design clear rules on tackling market concentration ex-ante, to be implemented by the Transmission System Operators.

Developing economies are in process of liberalizing their electricity markets, following similar process in developed economics. This process aims at establishing liquid energy exchanges that provide clear price signals, providing indications on the profitability of different operations: production, retail, trading in interconnections. This paper aims at developing a unit commitment model for examining zonal market pricing in Kazakhstan. The latter has an extensive landscape but sparsely populated, while is also characterized by the high availability of domestic fossil fuels, but located in different sub-regions of the country. The provision of zonal price signals in such a power system in invaluable, as it enables the provision of clear price signals on the needed infrastructure and the estimation of the zonal hourly energy and technology mix. Moreover, in enables the formation of dynamic bidding strategies by market participants in cases with favourable conditions, such as the implementation of scarcity pricing. This paper presents a unit commitment model that used to assess different bidding strategies and to provide zonal price signals. The strategies are formed, depending on the technology type and fuel prices comparison. Results provide clear signals on needed infrastructure among zones in Kazakhstan. It also shows that dynamic biding can lead to market coupling. Finally, it indicates the importance of institutional capability to monitor bidding strategies, eliminating speculation. Keywords: Electricity markets, unit commitment model, Kazakhstan JEL Classifications: Q4, Q47, L94 DOI: https://doi.org/10.32479/ijeep.9022

An accurate fuel consumption prediction system for transportation units is the pillar that a more efficient fuel management can rely on. This in turn may eventually lead to cost and emission savings for the unit’s owner. Numerous studies have been conducted for predicting the fuel usage in various means of transportation (i.e., airplanes, trucks, and vehicles). However, there is a limited number of researches that focus on passenger ships. These researches involve traditional machine learning models. There is a lack of literature on deep-learning-based forecasting models. The present paper serves as an initial study for exploring the potential of deep learning in day-ahead fuel consumption on a passenger ship. Firstly, a discussion is provided for the parameters that influence the fuel consumption. Secondly, the day-ahead fuel forecasting problem is formulated. To fully examine the influence of exogenous parameters on the consumption, various scenarios are formulated that differ in the types and number of inputs. The proposed forecasting model combines shallow and deep learning. Several machine learning and time series models were compared, and the results indicate the robustness of the proposed approach.

This work presents a generic mixed integer linear programming model to determine the optimal energy and reserves scheduling of a renewable-based multi-zonal power system. In particular, through a detailed unit commitment model implementing a co-optimization of energy and reserves market with a cost minimization objective function, the developed approach determines the optimal annual energy and reserves mix of the Romanian power system in a future year (2040). The model outputs highlight the impacts in the power system in terms of operational, economic, and environmental performance. The developed optimization framework enables the provision of useful insights on the determination of the optimal transition roadmaps, by highlighting the influences and the challenges of each phase.

The shale gas developments over the last two decades have challenged the gas price linkage with crude oil. The decoupling of the US wholesale gas from oil markets is mainly attributed to the rapid development of unconventional production, which formed a regional natural gas market based on regional market fundamentals. Moreover, investments in exporting facilities in the US made more quantities available to the rest of the world making global integration more plausible. This paper provides empirical evidence on the price and volatility transmission among the main European (NBP and TTF) and the Japan-Korean Marker (JKM) gas markets with that of Brent crude oil market, a crude oil benchmark used in Europe and Asia. The paper provides evidence that there are no price spillovers among oil and gas in European gas hubs. The European markets, contrary to the JKM market, seem to be mature enough as in the case of the US gas market. Finally, the paper provides policy recommendations on key elements for establishing functional gas hubs. Keywords: natural gas and oil markets; price and volatility spillovers; Europe, Japan JEL Classifications: Q40, Q41, C5 DOI: https://doi.org/10.32479/ijeep.9774

This work presents a generic optimization framework (ANNEX model), including three alternative algorithms for the electricity market-clearing process in order to optimally determine the annual energy mix of a power system. The effectiveness of the ANNEX model has been tested on an illustrative case study of the Bulgarian power system to investigate the impacts of each market design on the system's technical and economic aspects. The results highlight the influences of each market-clearing algorithm on the technology selection in the resulting electricity mix, as well as the effects on the system's marginal price, capturing both the annual dynamics and the short-term challenges of critical days. The developed methodological framework can provide useful insights on the determination of the optimal electricity mixes, highlighting the system's requirements to address the future market operating challenges of modern energy markets, subject to several technical, economic, and regulatory constraints.

With the exception of Albania, the countries of South-Eastern Europe (SEE) have high shares of electricity generation from an ageing fleet of coal-fired power units with quite low efficiencies. Energy planning-related decision-making regarding whether to modernize or replace a significant share of this old generating capacity has to be fixed within a global clean energy transition context. At the same time, the SEE region has a huge potential for renewable energy deployment and energy efficiency implementation. This work presents a generic mixed integer linear programming model to determine the optimal energy scheduling of a multi-zonal power system following the roadmaps of its energy transition process. Through a detailed unit commitment model implementing a co-optimization of energy and reserves market with a cost minimization objective function, the developed approach determines the optimal annual energy mixes of the Romanian power system through the day by day iterative solution of all the dates of three specific time milestones (2020, 2030, and 2040), where the outputs of a specific date comprise the inputs of the next one. The model outputs highlight the impacts on the power system in terms of technical, economic, and environmental performance. The energy transition towards a renewables dominant capacity and production mix guarantees the system’s security of supply, and improves noticeably its environmental performance, reporting a significant decrease in the amount of resulting CO2 emissions. With regard to the economic impacts, the wholesale price follows an increasing trend due to the parallel increase of the CO2 emission pricing, on the grounds that natural gas-fired units and electricity interconnections set the system’s price during most of the hours of all dates and years. The developed optimization framework enables the provision of useful insights on the determination of the optimal energy roadmaps, by highlighting the influences and the challenges of each phase.

To implement the process of European power markets’ integration, a market-clearing algorithm has been developed among European power exchanges under the title EUPHEMIA (Pan-European Hybrid Electricity Market Integration Algorithm), being a strictly economic dispatch algorithm and providing several options and products to market participants. This paper proposes an optimization-based methodological framework for the optimal economic dispatch problem in power exchanges, further enhancing the EUPHEMIA algorithm’s block order module. More specifically, through the development of a mixed-integer linear programming (MILP) model and utilizing an iterative process, it quantifies the impacts of the proposed new options to the optimal energy mix, the wholesale prices, and on the market players’ economic performance. The model considers all the current market products of the EUPHEMIA algorithm, as well as introduces new market products such as the flexibility provision of the main aspects of block orders (minimum acceptance ratio, price offer, and block time limits), the modification of existing block orders (exclusive groups), the development of new linkage structure (linked block orders with parallel relationship), as well as the activation of mixed schemes combining existing types of block orders into new integrated forms. The developed optimization framework has been assessed in the Greek interconnected power system, including its interconnections with neighboring power systems. The proposed methodological approach suggests a robust and systematic methodological formation to provide useful insights on policy issues and ongoing debate regarding the shaping of more efficient market designs and structures to deal with the new operational challenges of low-carbon flexible power systems.

The increasing interactions and interdependences of the wholesale and retail markets in the power sector have created the need for development of integrated approaches for the optimal portfolio management of vertically integrated utilities, aiming at drastically limiting their risk exposure. This work presents a generic mixed integer linear programming (MILP) model for the optimal clearing of a wholesale power market including market products widely used in power exchanges such as block and hourly orders. Based on the market clearing results, it then calculates the economic balance of vertically integrated utilities participating in retail markets, covering the whole value chain in the power sector. By considering a wide range of technology options in the wholesale market, including fossil fuel-based thermal units with or without carbon capture and sequestration capability (CCS), nuclear power, renewable energy, electricity trading and storage options, as well as different types of consumers in the retail market (low, medium and high voltage), the model determines the optimal electricity generation mix, the system’s marginal price and its environmental performance, as well as the net economic position of each power utility in both wholesale and retail markets. A series of sensitivity analyses on the CO2 emission pricing, the renewables’ penetration, and the applied environmental policy has been conducted to investigate the influence of several economic, technical and policy parameters on the operational scheduling and financial planning of each utility. The model applicability has been assessed in an illustrative case study of a medium-sized power system considering also its interconnections with neighboring power systems. The model results quantify the risk facing electric utilities participating in both wholesale and retail markets under several market conditions and with different schemes on the generation side, as well as with different representation shares on the demand side. The proposed optimization framework can provide useful insights on the determination of the optimal integrated generation and retail portfolios that address the new market operating challenges of contemporary power systems, subject to several technical and economic constraints, enabling also the design of medium-term operational strategies for vertically integrated power utilities.

This study aims to re-investigate the long-run relationship among energy prices and economic growth within the periphery of the European Union. We rely on the Engle–Granger methodology to estimate a Vector-Error Correction Model. We also employ Variance Decomposition Analysis to estimate the causal effect of energy prices on economic growth. We provide evidence on the conservation hypothesis for the case of real GDP and residential electricity prices, as well as on the growth hypothesis for the case of real output and industrial electricity prices. The residential electricity sector exhibits the highest level of influence, as industrial electricity price and crude oil price “Granger cause” residential electricity prices. We also find signs of the feedback hypothesis concerning final energy consumption and residential electricity price. Lastly, the level of economic growth proxied by real GDP is strongly endogenous in the short-run, whereas shocks from other covariates seem to have a transitory effect.