Search Results (1,450)

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and the power grid, V2G can support renewable energy accommodation, peak shaving, demand response, ancillary services, and local grid balancing. This review provides a systematic synthesis of recent advances in V2G systems for renewable energy integration, with particular emphasis on coordinated scheduling, economic mechanisms, battery degradation, and user engagement. First, the technical foundations of V2G are introduced, including Vehicle-to-Everything operating modes, bidirectional charging architecture, aggregation mechanisms, grid-support services, and renewable accommodation pathways. Second, major scheduling strategies are reviewed, including price-based, load-based, renewable-forecast-driven, centralized, distributed, and hybrid approaches. Third, the economic feasibility of V2G is examined from the perspectives of revenue streams, pricing mechanisms, business models, battery aging costs, and compensation schemes. In addition, user participation barriers, such as range anxiety, battery lifetime concerns, loss of control, uncertain financial returns, and data privacy, are discussed. Key challenges related to communication standards, interoperability, cybersecurity, market access, policy design, and pilot-scale validation are also summarized. Finally, future development directions are identified, including AI-based scheduling, aggregator platforms, fleet-scale V2G, degradation-aware optimization, carbon-aware electricity markets, and user-centered participation mechanisms. This review highlights that large-scale V2G deployment requires the integrated coordination of technical scheduling, economic incentives, battery health protection, and user acceptance in renewable-rich power systems. Full article

The increasing penetration of variable renewable energy sources (RESs) poses significant challenges to power system flexibility and reliability, particularly in systems with high solar generation. At the same time, existing Concentrating Solar Power (CSP) plants in Europe face declining economic viability due to high capital costs and the expiration of incentivized tariff schemes. This study proposes and evaluates a novel approach to repurpose CSP plants as flexible energy assets through the integration of cold thermal energy storage (CTES) within a Thermally Integrated Power-to-Heat-to-Power Energy Storage (TI-PTES) framework. The proposed system combines an ice/water-based cold storage with a CO₂-based refrigeration cycle to enhance the efficiency of the CSP steam cycle by reducing condenser temperatures, while also enabling temporal shifting of electricity consumption. A techno-economic optimization model based on PyPSA is developed to determine the optimal sizing and operation of the storage and refrigeration system under realistic load and electricity price conditions representative of the Spanish market. Results show that the integration of cold storage significantly alters system operation, shifting the chiller from a continuous demand-following mode to an intermittent, high-intensity regime. This leads to a reduction in annual operating expenditures by approximately 32% and an increase in annual profit and net present value (NPV), despite higher capital investment. While hourly net revenue becomes more volatile, with negative values during charging periods, cumulative annual performance improves due to effective temporal optimization. However, the absence of strong electricity price arbitrage and negative price signals limits the revenue potential of the storage system, which primarily acts as a cost-reduction mechanism. The findings demonstrate that cold thermal storage can successfully reposition CSP plants as flexible, value-generating assets in modern electricity systems. The proposed concept offers a promising pathway for extending the operational lifetime of existing CSP infrastructure while supporting higher integration of renewable energy sources. Full article

We develop a stochastic Stackelberg model of a subscription market with cancellation costs. A representative consumer chooses when to subscribe to and cancel a service as the utility derived from the subscription evolves according to a diffusion process, while the firm selects the subscription fee and cancellation cost to maximize its expected payoff. The consumer’s problem is equivalent to the classical real-options model of entry and exit under uncertainty with adjustment costs and exhibits a two-threshold policy with an inaction band and hysteresis. Unlike the standard formulation, in which the optimal thresholds are characterized implicitly through a system of nonlinear equations, we derive an explicit parametric solution in closed form. This solution reduces the firm’s optimization problem to a two-dimensional unconstrained problem and yields a detailed characterization of the optimal pricing policy. We show that the firm’s strategy exhibits three qualitatively distinct regimes depending on the initial utility level. For small utility levels, the optimal cancellation cost is zero. In an intermediate regime, the firm’s optimal policy induces the consumer to set the entry threshold equal to the initial utility level, resulting in immediate subscription. For sufficiently large utility levels, the firm induces permanent lock-in by setting a high cancellation cost and a low subscription fee: the consumer subscribes immediately and never subsequently unsubscribes. The transition between the latter two regimes is discontinuous and results from competition between two local maxima of the firm’s payoff function. We then extend the model to a heterogeneous population of consumers. The superposition of individual two-threshold subscription strategies generates a Preisach hysteresis operator describing the aggregate dependence of the firm’s revenue on the utility dynamics. The discontinuous regime transition persists under heterogeneity, demonstrating the robustness of the underlying mechanism. The Preisach representation predicts complex history dependence and long-term effects of temporary utility shocks. For a gamma distribution of consumer preferences, the firm’s expected payoff is obtained in closed form in terms of incomplete gamma functions. Full article

As uncertainty increases within the aviation industry, the importance of airport sustainability has come to the forefront, necessitating continuous demand generation through strategic aviation marketing. Consequently, there is an urgent need to analyze the practical efficacy of the incentive programs implemented primarily by global airports. In particular, amid intense competition among Northeast Asian airports to attract airlines, this study empirically analyzes the effects of Incheon International Airport’s (ICN) incentive programs on airline supply and passenger demand growth. To this end, utilizing empirical data from ICN spanning 2016 to 2024, this research quantitatively establishes the causal mechanism between incentive programs, supply expansion, and demand growth. Specifically, it comprehensively evaluates the impact of incentives provided for new airline entry and new route expansion, while comparing the effects across different airline business models. The empirical results confirm that the effects vary significantly depending on the type of incentive program and the airline category. Furthermore, the findings indicate that airport incentives are not merely sunk costs, but rather strategic investments that generate measurable aviation demand and airport revenue. However, the analysis also suggests a need to supplement and refine these incentive schemes to tailor them to specific airline types. Full article

Many Greek islands host geothermal springs whose waters can be desalinated to produce drinking water. Some of these waters contain meaningful concentrations of the valuable Rb⁺ and Cs⁺ ions, which, when extracted from the desalination brine as RbCl and CsCl salts, can yield revenues exceeding the freshwater production costs. We demonstrate the use of reverse osmosis (RO) to produce freshwater and apply theoretical simulations to assess a proven extraction method applied to the RO retentate of geothermal water from Samothrace, characterized by [Rb⁺] = 2.72, [Cs⁺] = 0.55, [K⁺] = 514, [Na⁺] = 3759 (all in mg/L) and pH 6. The extraction method, developed by the authors, relies on ion exchange using a PES-coated Zn-hexacyanoferrate sorbent with high affinity for monovalent cations (no affinity for multi-valent cations), followed by a unique ion-chromatography separation. We show that the production cost remains <25% of the salts’ market price, with ROI of ~4.5 years. Full article

This study addresses the increasing need for flexibility in the Polish Power System (PPS), particularly in the context of growing price volatility on the Day-Ahead Market (DAM) resulting from the rising share of renewable energy sources (RESs). The aim of the study was to assess the feasibility of implementing demand-driven operation in an existing linear biogas plant in Poland and to develop a Decision-Making Model (DMM) for optimizing its operation based on electricity price forecasts. A machine learning model based on Extreme Gradient Boosting (XGBoost) was developed using historical electricity price, demand and weather data and integrated into the DMM to generate hourly operating schedules. The model achieved high predictive accuracy, with a Mean Absolute Error (MAE) of approximately 51 PLN/MWh, and effectively captured nonlinear price dynamics. Based on predefined decision thresholds—biogas production rate, current biogas storage level, upper and lower limits of pressure in biogas storage capacity, maximum biogas storage duration, power quotient (PQ) and electricity price levels—optimal operating strategies were determined. The results indicate that while demand-driven operation is technically feasible and enables better alignment with market signals, its economic viability remains limited under current market and regulatory conditions. Investment in additional cogeneration capacity was not justified, as costs significantly exceeded potential revenues. Consequently, a more viable approach involves optimizing existing infrastructure through flexible production strategies. Full article

The efficiency of scientific and technological achievement transformation is constrained by supply–demand matching challenges. Concurrently, Artificial Intelligence (AI) offers novel pathways for digital-intelligence service platforms to mitigate this challenge. To resolve AI investment decision problems of such platforms, this study constructs a bilateral matching model involving high-quality/low-quality technology providers and high-capability/low-capability technology seekers. Based on expected value theory and Stackelberg games, it derives optimal AI investment strategies for the Commercial Platform (platform’s expected revenue maximisation objective) and the Public Welfare Platform (social expected revenue maximisation objective). Findings indicate that higher AI investment contributes to a rise in the matching probability between high-quality providers and high-capability demanders. Owing to incomplete benefit internalization, platforms of different types show divergent willingness for AI investment. The AI investment level of the Commercial Platform is lower than that of the Public Welfare Platform, which results in losses of expected matching value. Furthermore, declines in AI technology costs and reduced external selection value of suppliers will drive platforms to raise their AI investment intensity. This research provides theoretical foundations for optimising AI strategies in online technology transfer service platforms and informing targeted government interventions. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

Windthrow and the associated damage to forests have significant economic, social, and ecological impacts including increased harvesting costs and lost revenue, safety concerns for forest workers, and restriction on public access. The impacts of wind damage also directly affect greenhouse gas profiles associated with forest lands. This paper describes a two-stage forest monitoring approach that was devised for the purposes of assessing the impacts of the storms of winter 2024/2025, which included Storms Darragh and Éowyn, on the Irish forest estate. A range of Earth Observation (EO) datasets were used to assess the extent of windthrow damage within both public and private forests across the Republic of Ireland. The total area damaged was ca. 27,400 ha out of a total forest area of ca. 800,000 ha mainly affecting the north-west of the country. Based on scenarios developed to analyse the level of harvest in conjunction with the salvage operations, it was found that there was a decline in the sink capacity of the forest estate over the period 2025–2030. However, beyond this period, the sink capacity is restored as a result of the regeneration of the forests. Full article

Steel production accounts for approximately 7% of global GHG emissions. Brazil is the largest steel producer in Latin America, and carbon pricing is rapidly moving from a policy debate to an operational reality, making the financial exposure of Brazilian steelmakers to carbon regulation one of the most pressing industrial sustainability questions in an emerging market context. This study evaluates the financial exposure of the Brazilian steel industry to three carbon pricing scenarios: (i) a domestic cap-and-trade mechanism under Brazil’s Greenhouse Gas Emissions Trading System (SBCE); (ii) Carbon Border Adjustment Mechanisms (CBAMs) applied by key trading partners (EU, a hypothetical USA scenario, and a global scenario); and (iii) supply chain (Scope 3) exposure, relevant under net-zero corporate commitments and the expected expansion of EU-CBAM coverage. Using a scenario-based financial impact approach, with both macro-level (industry) and micro-level (company) analyses, results show that domestic carbon pricing could increase production costs by 7–21%, generating USD 1.6–4.9 billion in additional annual costs (equivalent to 4.3–13.3% of annual industry revenue). International CBAM exposure could reduce Brazilian steel export revenues by USD 570 million to USD 1.7 billion in a global scenario (1.5–4.6% of annual industry revenue). Supply chain emissions represent 68% of the industry’s total carbon pricing exposure, equivalent to USD 1.1–3.3 billion in domestic pricing costs and USD 388 million–1.16 billion in CBAM export revenue reduction. A company-level case study confirms the pattern, with lower Scope 3 intensity yielding a comparatively smaller but still material exposure. These findings offer practical decision support for steel companies and policymakers navigating the transition to a low-carbon economy. Full article

Urban logistics supports retail, public services, healthcare, construction, and household consumption, but delivery decisions often fail to account for the social costs imposed on congested roads, curbside space, air quality, noise exposure, safety, and public-space use. This study addresses the decision-maker mismatch that arises when the vehicle operator physically generates an externality while receivers, shippers, platforms, building managers, or consumers control delivery timing, shipment fragmentation, service level, fleet choice, and receiving conditions. It develops a Pigouvian policy framework that integrates dynamic road-user charging, curbside pricing, emission-based instruments, off-hour delivery incentives, clean-vehicle support, consolidation incentives, and revenue recycling. The study combines a structured narrative synthesis, decision-maker mapping, an illustrative Korean urban logistics scenario, cost–benefit comparison, and deterministic sensitivity screening. Under the stated scenario assumptions, a carrier-only peak charge reduces monetized daily external costs by only 1.3%, whereas a combined road-and-curbside package reduces them by 12.5%, and an integrated Pigouvian package reduces them by 23.6%. Sensitivity results preserve this ranking. The paper contributes a transferable policy design architecture for internalizing urban logistics externalities while maintaining freight functionality and stakeholder acceptability. Full article

The Engineering-Procurement-Construction (EPC) general contracting model has emerged as the dominant delivery method for large-scale infrastructure and industrial projects in China. However, contemporary EPC project cost control remains plagued by critical industry challenges, including fragmented cross-stage coordination, pervasive data silos, and the shallow integration of digital technologies into core management processes. This study considers three key stakeholders—government regulators, project owners, and EPC general contractors—and develops a tripartite evolutionary game model to analyze the strategic interactions underlying intelligent cost control in EPC projects. We examine the evolutionary stability of each stakeholder’s strategy selection, explore how various factors influence tripartite strategic choices, and further investigate the stability of equilibrium points in the game system. The key findings are summarized as follows: (1) Strengthening government incentives and penalties simultaneously promotes owners’ investment in intelligent cost control systems and general contractors’ active collaborative cost management. However, excessive incentive intensity undermines the government’s regulatory effectiveness. (2) Establishing a revenue-sharing mechanism for excess cost savings fully stimulates the spontaneous cooperation willingness of owners and general contractors, serving as the cornerstone for market-oriented operation of intelligent cost control. (3) Reducing owners’ intelligent construction investment costs and general contractors’ collaborative control costs effectively addresses practical implementation barriers and accelerates the digital upgrading of engineering cost management. Finally, numerical simulations are performed using MATLAB R2020b to validate theoretical findings. Full article

The transition to electric bus (EB) fleets is a critical step towards sustainable urban transportation, offering substantial reductions in greenhouse gas and pollutant emissions relative to diesel buses. However, transit authorities face multifaceted challenges in this transition, including limited driving ranges of EBs, the need for widespread charging infrastructure, and potential strain on the electric grid, alongside opportunities such as governmental subsidies and increased fare revenues. This paper proposes a comprehensive multi-period mixed-integer programming model seeking to optimize long-term EB fleet transition plans in urban contexts while jointly accounting for all inherent financial, technical, and operational factors impacting such a transition. The model is operationalized using real data acquired from Dubai’s Roads & Transport Authority (RTA), encompassing 71 bus routes and a 25-year planning horizon to meet a 100% electrification target by 2050. A scenario-based analysis evaluates the robustness of the transition plans under variations in key operational parameters. The results illustrate that optimized long-term planning yields substantial cost savings and emissions reductions, where the incorporation of environmental and social externalities and revenue shifts causes profit maximization to emerge as a more appropriate objective. In addition, it turns out that adequate dwell time is crucial for cost containment and full fleet electrification feasibility. While RTA targets 100% electrification by 2050, the base case is deliberately relaxed to 90% as certain routes, notably double-decker lines, are incompatible with currently available EB configurations. Nevertheless, full electrification is restored under the minimum dwell scenario. Also, a policy of purchasing only EBs accelerates full fleet electrification by roughly a decade with only a marginal increase in total cost, unlike imposing strict interim electrification targets. The optimized transition plans provide actionable insights for transit authorities balancing economic efficiency with sustainability goals. Full article

Rural electrification in Sub-Saharan Africa faces a trilemma: cutting carbon emissions, making it economically viable, and achieving fair access to energy for all. This paper develops a multi-objective framework that optimises carbon revenue, net present value (NPV), total energy supply, cooking fuel (firewood and LPG), health costs, and benefit to society. The model uses continuous decision variables: daily energy allocation among four sources (solar, generator, firewood, LPG) to three population groups (men, women, children). The case study is a rural community of 7000 people in Nigeria (Tier 1 energy consumers). Six policy scenarios are considered: baseline, high carbon price, low carbon price, microfinance, government subsidy and community cooperative. This study compared algorithms and identified a hybrid Non-dominated Sorting Genetic Algorithm and Particle Swarm Optimisation II as the most suitable algorithm for solving the formulated optimisation problem. It was found that NPV and unit cost of energy would increase to $175,500 and 26.4 ¢/kWh, respectively, by increasing the price of carbon from $8/ton to $12/ton. Firewood generates health savings and carbon revenue in the range of $4100–$12,270/year. Prices below $8/ton do not induce optimal reconfigurations in the system. The best energy supply (2825 kWh/day) and the lowest unsatisfied demand occur in the government subsidy scenario with the greatest disparity index, displaying an equity-efficiency trade-off. The framework shows that sustainable access to energy can be unlocked using strategic integration of carbon finance, valuation of health benefits and equity constraints. Full article

Energy storage systems (ESSs) installed alongside traditional photovoltaic (PV) power plants are primarily used to track planned output, which often results in low utilization rates and extended payback periods. Moreover, existing research inadequately addresses actual grid frequency fluctuation characteristics and lacks multi-timescale optimization frameworks. To address these issues, this paper proposes a day-ahead and intraday multi-market coordinated rolling optimization strategy that integrates energy market trading with Automatic Generation Control (AGC) frequency regulation services through a zone-based frequency regulation control strategy. The strategy first defines distinct regulation zones based on regional control deviations, enabling a dynamic power allocation approach for the energy storage system. Recognizing that conventional constant power control can lead to battery overcharging, over-discharging, and reduced cycle life, the strategy introduces state of charge (SOC)-based variable power charging and discharging constraint coefficients. These constraints ensure the battery operates safely within its optimal range. Furthermore, an electrochemical energy storage life decay model is developed to quantify battery degradation. To accommodate the uncertainty in PV output, Latin hypercube sampling is employed. A day-ahead dispatch model is established to maximize the system’s total daily operating revenue, and rolling optimization is applied during the intraday phase to correct deviations from the day-ahead forecast. Finally, simulation studies using actual data from a PV power plant demonstrate that the proposed strategy achieves a total daily revenue of 107,477 ¥, representing a 24.6% improvement over energy market-only participation; battery aging costs are reduced by 11.1% compared to the scenario without zone-based frequency regulation control. Results indicate that the proposed strategy effectively balances battery life degradation against market revenue, significantly improving the overall operational efficiency and economic viability of PV-storage hybrid systems. Full article