Energy Market Scenarios Research Articles

Virtual Power Plant for Optimizing Power Flow in Large Scale Distributed Networks

The control issues in the network caused by the increasing proportion of dispersed production & sources of clean energy need coordinated handling of these assets. Virtual power plants (VPPs) may be thought of as systems that comprise resources like electric vehicles, energy storage, controlled loads, and other distributed generators. Operation, resource unpredictability, energy management, involvement in electrical markets, etc. are just a few of the many obstacles VPPs must overcome. The challenge of the study is to optimize the performance of VPPs in managing dispersed clean energy sources, and the proposed model employs a distributed control approach and particle swarm optimization (PSO)-based learning reinforcement technique to address this challenge by maximizing VPP output and operational efficiency in various energy market scenarios. In this research, we explore the topic of ongoing electrical power in massively distributed systems based on VPPs. To maximize the VPP output & reach an ideal operational point, the authors of this work used a distributed control approach. The purpose of this article is to examine VPP performance under a variety of markets for energy configurations. Further, a PSO-based learning reinforcement technique is used to examine the benefits and drawbacks of VPPs’ power management.

Coordinated framework for active distribution system planning integrating active system management schemes

AbstractTo counter different technical challenges with large‐scale renewable energy sources (RESs) integration, distribution systems (DS) are recently undergoing the transition process from passive to active DS (ADS), which has introduced further complications for system planning. In such context, this study proposes an ADS expansion planning (ADSEP) framework in coordination with active system management (ASM) schemes. A multi‐stage ADSEP model, unlike the traditional single‐stage model, is designed where the planning horizon is divided into several stages to make the planning investments gradually. For proper coordination of different planning alternatives, an effective bi‐level model is formulated based on upper‐level and lower‐level planning. In accordance with recent energy market scenario, RESs are assumed to be invested by RES Investor (RESI) and to ensure the desired financial benefits of RESI, the Distribution Company (DISCOM) offers bus‐wise contract price (BCP) for purchasing RESs generated energy. Moreover, a computationally efficient 2 m point estimate method (PEM) is applied to model the uncertainties of load and RESs. The proposed planning framework with multiple objectives is solved using the multi‐objective PSO technique considering 69‐bus test system.

Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques

Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.

Robust Design of a Fuel Cell—Turbocharger Hybrid System

Abstract Pressurized solid oxide fuel cell (SOFC) systems are a particularly attractive conversion technology for their high electric efficiency, potential for cogeneration applications, low carbon emissions, and high performance at part-load. In this work, an innovative biofueled hybrid system is considered, where the fuel cell stack is pressurized with a turbocharger, resulting in a system with improved cost effectiveness than a microturbine-based one at small scales. In a previous work, a detailed steady-state model of the system, featuring components validated with industrial data, was developed to simulate the system and analyze its behavior in different conditions. The results obtained from this model were used to create response surfaces capable of evaluating the impact of the main operating parameters (fuel cell area, stack current density, and recuperator (REC) surface) on the performance and the profitability of the plant considering system uncertainties. In this paper, similar but extended response surfaces will be used to perform a multi-objective optimization of the system considering the capital costs of the plant and the net power produced as objectives (turbocharger is fixed in geometry). The impact of the energy market scenario on the optimal design of such a system will be investigated considering its installation in three different countries. Finally, the Pareto front produced by optimization will be used to evaluate the robustness of the top performance solutions.

Thermo-Economic Assessment Under Electrical Market Uncertainties of a Combined Cycle Gas Turbine Integrated With a Flue Gas-Condensing Heat Pump

Abstract Renewable energy penetration is growing, due to the target of greenhouse-gas-emission reduction, even though fossil fuel-based technologies are still necessary in the current energy market scenario to provide reliable back-up power to stabilize the grid. Nevertheless, currently, an investment in such a kind of power plant might not be profitable enough, since some energy policies have led to a general decrease of both the average single national price of electricity (PUN) and its variability; moreover, in several countries, negative prices are reached on some sunny or windy days. Within this context, combined heat and power (CHP) systems appear not just as a fuel-efficient way to fulfill local thermal demand but also as a sustainable way to maintain installed capacity able to support electricity grid reliability. Innovative solutions to increase both the efficiency and flexibility of those power plants, as well as careful evaluations of the economic context, are essential to ensure the sustainability of the economic investment in a fast-paced changing energy field. This study aims to evaluate the economic viability and environmental impact of an integrated solution of a cogenerative combined cycle gas turbine power plant with a flue gas condensing heat pump. Considering capital expenditure, heat demand, electricity price, and its fluctuations during the whole system life, the sustainability of the investment is evaluated taking into account the uncertainties of economic scenarios and benchmarked against the integration of a cogenerative combined cycle gas turbine power plant with a heat-only boiler (HOB).

Integration of a flue gas condensing heat pump within a combined cycle: Thermodynamic, environmental and market assessment

Although considered as the bridging technology to a decarbonized economy, in the current energy market scenario Combined Cycle Gas Turbine (CCGT) must deal with more demanding requirements of efficiency and flexibility, and often they are not profitable enough to avoid mothballing or closure. The purpose of this work is to propose an innovative solution to enhance the plant flexibility, integrating a combined heat and power CCGT with a flue gas condensing heat pump. The paper firstly evaluates the thermodynamic performances of two possible plant layouts coupled with a District Heating Network, illustrating the respective advantages and disadvantages under different operating conditions. Then, in order to assess the viability of the investment the best solution is selected and a thermoeconomic analysis is performed using as benchmark the same CCGT integrated with a natural gas fed Heat Only Boiler in, a cheaper state of art solution but with a higher carbon footprint. Different energy market scenarios are assessed, considering the effect of different parameters on the integrated CCGT’s final profitability. On the environmental side, the heat pump allows reaching an higher global efficiency (average annual efficiency increase up to 5 percentage points), and reducing the annual CO2 emission (−13.9% in the best case). On the economic side, the higher heat pump capital expenditure, with respect to the heat only boiler limits the field of viability of the investment to high gas to electricity price ratios. In particular, the minimum ratio between the cost of natural gas and the average cost of electricity that make the solution viable was found to be between 0.7 and 0.8, a ratio envisaged in case of large RES production share in the grid network. The proposed layout can be of interest for already existing or new CCGT coupled with an heat demand in the temperature range (80–150 °C) as a power to heat solution able to increase the overall system efficiency.

Bottom–Up Assessment Framework for Electrification Options in Energy-Intensive Process Industries

Electrification of industrial processes is one of the frequently discussed options to reduce greenhouse gas emissions from energy-intensive industries. This paper presents a bottom-up framework to assess process electrification options for energy-intensive industrial process plants in terms of greenhouse gas emissions and energy costs. The framework is based upon pinch analysis methods, and accounts for site-specific conditions, including the effects on heat recovery potential and overall mass and energy balances. Furthermore, interactions between the process site and the background energy system are considered and scenarios are introduced in order to assess the impact of electrification options under different future energy market conditions. The framework is illustrated by a case study for an existing chemical plant for which there is a broad variety of electrification options that affect the process in different ways. The option of replacing the natural gas based syngas production unit with electrified syngas and electrified steam production is analysed in detail. The results indicate natural gas savings of 173 MW whereas the electricity demand increases up to 267 MW, leading to a strong increase in energy costs but also avoided greenhouse gas emissions of 333 kt/a. For the two energy market scenarios for 2030 and 2040, the energy costs increase by 59M€/a and 50M€/a. The framework can be used to compare electrification with other process greenhouse gas emission reduction measures and to support policy and industrial decision making.

Integration of algae‐based biofuel production with an oil refinery: Energy and carbon footprint assessment

Biofuel production from algae feedstock has become a topic of interest in the recent decades since algae biomass cultivation is feasible in aquaculture and does therefore not compete with use of arable land. In the present work, hydrothermal liquefaction of both microalgae and macroalgae is evaluated for biofuel production and compared with transesterifying lipids extracted from microalgae as a benchmark process. The focus of the evaluation is on both the energy and carbon footprint performance of the processes. In addition, integration of the processes with an oil refinery has been assessed with regard to heat and material integration. It is shown that there are several potential benefits of co-locating an algae-based biorefinery at an oil refinery site and that the use of macroalgae as feedstock is more beneficial than the use of microalgae from a system energy performance perspective. Macroalgae-based hydrothermal liquefaction achieves the highest system energy efficiency of 38.6%, but has the lowest yield of liquid fuel (22.5 MJ per 100 MJalgae) with a substantial amount of solid biochar produced (28.0 MJ per 100 MJalgae). Microalgae-based hydrothermal liquefaction achieves the highest liquid biofuel yield (54.1 MJ per 100 MJalgae), achieving a system efficiency of 30.6%. Macro-algae-based hydrothermal liquefaction achieves the highest CO2 reduction potential, leading to savings of 24.5 resp 92 kt CO2eq/year for the two future energy market scenarios considered, assuming a constant feedstock supply rate of 100 MW algae, generating 184.5, 177.1 and 229.6 GWhbiochar/year, respectively. Heat integration with the oil refinery is only possible to a limited extent for the hydrothermal liquefaction process routes, whereas the lipid extraction process can benefit to a larger extent from heat integration due to the lower temperature level of the process heat demand.

Definition of LCA Guidelines in the Geothermal Sector to Enhance Result Comparability

Geothermal energy could play a crucial role in the European energy market and future scenarios focused on sustainable development. Thanks to its constant supply of concentrated energy, it can support the transition towards a low-carbon economy. In the energy sector, the decision-making process should always be supported by a holistic science-based approach to allow a comprehensive environmental assessment of the technological system, such as the life cycle assessment (LCA) methodology. In the geothermal sector, the decision-making is particularly difficult due to the large variability of reported results on environmental performance across studies. This calls for harmonized guidelines on how to conduct LCAs of geothermal systems to enhance transparency and results comparability, by ensuring consistent methodological choices and providing indications for harmonized results reporting. This work identifies the main critical aspects of performing an LCA of geothermal systems and provides solutions and technical guidance to harmonize its application. The proposed methodological approach is based on experts’ knowledge from both the geothermal and LCA sectors. The recommendations cover all the life cycle phases of geothermal energy production (i.e., construction, operation, maintenance and end of life) as well as a selection of LCA key elements thus providing a thorough base for concerted LCA guidelines for the geothermal sector. The application of such harmonized LCA framework can ensure comparability among LCA results from different geothermal systems and other renewable energy technologies.

Economic potential for substitution of fossil fuels with liquefied biomethane in Swedish iron and steel industry – Synergy and competition with other sectors

In Sweden, the iron and steel industry (ISI) is a major source of greenhouse gas (GHG) emissions. Most of the emissions result from the use of fossil reducing agents. Nevertheless, the use of fossil fuels for other purposes must also be eliminated in order to reach the Swedish emissions reduction targets. In this study, we investigate the possibility to replace fossil gaseous and liquid fuels used for heating in the ISI, with liquefied biomethane (LBG) produced through gasification of forest residues. We hypothesize that such utilization of fuels in the Swedish ISI is insufficient to independently drive the development of large-scale LBG production, and that other sectors demanding LBG, e.g., for transportation, can be expected to influence the economic potential for the ISI to switch to LBG. The paper investigates how demand for LBG from other sectors can contribute to, or prevent, a phase-out of fossil fuels used for heating purposes in the ISI under different future energy market scenarios, with additional analysis of the impact of a CO2 emissions charge. A geographically explicit cost-minimizing biofuel production localization model is combined with heat integration and energy market scenario analysis. The results show that from a set of possible future energy market scenarios, none yielded more than a 9% replacement of fossil fuels used for heating purposes in the ISI, and only when there was also a demand for LBG from other sectors. The scenarios corresponding to a more ambitious GHG mitigation policy did not achieve higher adoption of LBG, due to corresponding higher biomass prices. A CO2 charge exceeding 200 EUR/tonCO2 would be required to achieve a full phase-out of fossil fuels used for heating purposes in the ISI. We conclude that with the current policy situation, substitution of fossil fuels by LBG will not be economically feasible for the Swedish ISI.

Pathways to low-cost clean hydrogen production with gas switching reforming

Gas switching reforming (GSR) is a promising technology for natural gas reforming with inherent CO2 capture. Like conventional steam methane reforming (SMR), GSR can be integrated with water-gas shift and pressure swing adsorption units for pure hydrogen production. The resulting GSR-H2 process concept was techno-economically assessed in this study. Results showed that GSR-H2 can achieve 96% CO2 capture at a CO2 avoidance cost of 15 $/ton (including CO2 transport and storage). Most components of the GSR-H2 process are proven technologies, but long-term oxygen carrier stability presents an important technical uncertainty that can adversely affect competitiveness when the material lifetime drops below one year. Relative to the SMR benchmark, GSR-H2 replaces some fuel consumption with electricity consumption, making it more suitable to regions with higher natural gas prices and lower electricity prices. Some minor alterations to the process configuration can adjust the balance between fuel and electricity consumption to match local market conditions. The most attractive commercialization pathway for the GSR-H2 technology is initial construction without CO2 capture, followed by simple retrofitting for CO2 capture when CO2 taxes rise, and CO2 transport and storage infrastructure becomes available. These features make the GSR-H2 technology robust to almost any future energy market scenario.

Consumer viewpoint on a new kind of energy market

Energy markets are currently evolving due to the rapid digitalization, increasing amount of renewable energy sources, and rapid technology development. Market actors are being forced to adapt their business models to these changes or find new business opportunities. In our earlier work, new change scenarios were identified that reflect the future business potential of Finnish companies in energy domain particularly with respect to demand flexibility, new actor roles, new consumer services and novel activities in a new kind of energy market. The purpose of this paper is to examine the energy consumer viewpoint on these new scenarios. As consumers are becoming more central actors in the energy markets via demand flexibility and new type of services, it is critical to design the solutions in a user-centric way. A consumer survey was conducted to identify consumers’ opinions and attitudes and determine their willingness to participate in the new energy market scenarios. The survey reached a large number of consumers representing a considerable portion of the Finnish population. According to the results, a half of consumers consider the forecasted scenarios as positive changes, but a lot of communication must still be done in order to win the majority support of consumers.

A binary‐regularization‐based model predictive control applied to generation scheduling in concentrating solar power plants

SummaryThis paper proposes the use of model predictive control (MPC) with binary‐regularization to manage the electric power generation problem in concentrating solar power plants with thermal energy storage. The main advantage of the of MPC with binary‐regularization formulation is the inclusion of a power block protection method based on a binary‐regularization term that penalizes power generation variation (also called generation cycling) differently according to the power block situation, ie, normal operation, startup, or shutdown. This distinction simplifies the choice of schedules with reduced variation and high energy sale profits. The interest in this reduction is the achievement of a higher lifetime of the power block elements, lower maintenance costs, and easier plant operability. A benefit of the generation scheduling based on MPC is the capacity of rescheduling the power generation at regular periods, taking advantage of the most recent energy prices and weather forecast, and of the plant's current state. An interesting question is if the proposed protection mechanism affects the economic results of the MPC black strategy. In this regard, an economic study based on a realistic simulation of a 50 MW parabolic trough collector‐based concentrating solar power plant with thermal energy storage, under the assumption of participation in the Spanish day‐ahead energy market scenario, is included. Realistic values for actual and forecasted solar resource and for energy price are used, and for penalties for deviation from the committed generation schedule. The economic study shows that the proposed scheduling method provides an important reduction of the generation cycling without decreasing energy sales profits. Another advantage of the proposed method is the possibility of estimating the highest level of power block protection, which maintains the profits by means of historical data, which favors its practical implementation.

Techno-economic analysis of battery storage systems for demand responds application in manufacturing

Abstract Demand response mechanisms are considered a cost-effective strategy to increase the reliability of electricity grids and reduce the need for grid extension. The objective of the presented research is to conduct a techno-economic analysis for the use of battery storage systems in decentralized demand responds applications in manufacturing. In order to perform the analysis, different energy market scenarios were developed and a parameter study was conducted using simulation modelling. The results show that none of the 78 individual combinations considered in the simulation experiment represent an economically viable investment scenario. However, small installed storage capacities perform superior to large installations in terms of net present value. The performed sensitivity analysis underlines that parameter sets for the battery systems, energy market models and control strategies require further research in order to improve the reliability of the simulation results. Aside from the techno-economic performance, the use of battery storage systems in decentralized demand responds applications need to be assessed in terms of its environmental impact.

Profitability and greenhouse gas emissions of gasification-based biofuel production - Analysis of sector specific policy instruments and comparison to conventional biomass conversion technologies

The required level of a sector specific CO2e-cost in the transport sector to make the net annual profit (NAP) of three different gasification based biofuel production systems positive (systems profitable) is investigated. The analysis is made for two different energy market scenarios for 2030 and 2040. The results show that the additional required sector specific CO2e-cost (additional to a sector wide general cost) is not higher than the current level of CO2e-tax in Sweden. The required total level of CO2e-cost for the transport sector is in the 450 ppmv scenario in general higher than the current CO2-tax level but not higher than the fuel tax level (including also energy tax).The study also compares the NAP and greenhouse gas (GHG) emission reduction potential of the gasification-based systems to a system where the biomass is used in conventional bio-CHP to produce heat and power and where the power is used in the transport sector (in battery electric vehicles (BEV)). Under the investigated energy market scenarios the bio-CHP and BEV system has higher NAP and higher GHG emission reduction potential. However, the bio-CHP system has a stronger dependency on the availability of large heat sinks and profits from a high price of delivered heat.

Integrating Economic and Ecological Benchmarking for a Sustainable Development of Hydropower

Hydropower reservoirs play an increasingly important role for the global electricity supply. Reservoirs are anthropogenically-dominated ecosystems because hydropower operations induce artificial water level fluctuations (WLF) that exceed natural fluctuations in frequency and amplitude. These WLF have detrimental ecological effects, which can be quantified as losses to ecosystem primary production due to lake bottoms that fall dry. To allow for a sustainable development of hydropower, these “ecological costs” of WLF need to be weighed against the “economic benefits” of hydropower that can balance and store intermittent renewable energy. We designed an economic hydropower operation model to derive WLF in large and small reservoirs for three different future energy market scenarios and quantified the according losses in ecosystem primary production in semi-natural outdoor experiments. Our results show that variations in market conditions affect WLF differently in small and large hydropower reservoirs and that increasing price volatility magnified WLF and reduced primary production. Our model allows an assessment of the trade-off between the objectives of preserving environmental resources and economic development, which lies at the core of emerging sustainability issues.

Comparison of integration options for gasification-based biofuel production systems – Economic and greenhouse gas emission implications

The impact of different integration options for gasification-based biofuel production systems producing synthetic natural gas, methanol and FT (Fischer-Tropsch) fuels on the NAP (net annual profit), FPC (fuel production cost) and the GHG (greenhouse gas) emission reduction potential are analysed. The considered integration options are heat deliveries to DH (district heating) systems or to nearby industries and integration with infrastructure for CO2 storage. The comparison is made to stand-alone configurations in which the excess heat is used for power production. The analysis considers future energy market scenarios and case studies in southwestern Sweden. The results show that integration with DH systems has small impacts on the NAP and the FPC and diverging (positive or negative) impacts on the GHG emissions. Integration with industries has positive effects on the economic and GHG performances in all scenarios. The FPCs are reduced by 7–8% in the methanol case and by 12–13% in the FT production case. The GHG emission reductions are strongly dependent on the reference power production. The storage of separated CO2 shows an increase in the GHG emission reduction potential of 70–100% for all systems, whereas the impacts on the economic performances are strongly dependent on the CO2e-charge.

Optimal Consumption Policies for Power-Constrained Flexible Loads Under Dynamic Pricing

This paper analyzes the response of an individual consumer with a flexible demand for electricity to exogenous and stochastic electricity prices. The contributions of this paper are twofold. First, we propose a comprehensive model for which the optimal policy can be explicitly computed. The proposed consumer model features a time varying bound on power consumption, the presence of a nondeferrable load profile, the presence of a load profile that can be deferred up to a deadline, and the possibility of curtailment. Second, we describe the algorithm that, via explicit backward iterations of the Bellman equation, returns the optimal response of such consumer in a very general energy market scenario featuring a correlated/nonstationary price process and multiple energy procurement sources.

Effect of the use of industrial excess heat in district heating on greenhouse gas emissions: A systems perspective

European policy promotes increased use of excess heat as a means to increase the efficiency of resource use. By studying possible effects on greenhouse gases, this article aims to analyze and discuss system aspects of the use of industrial excess heat in district heating. Effects on greenhouse gas emissions are studied by applying different energy market conditions with different system boundaries in time and space. First, life cycle assessment is used to assess the introduction of excess heat in district heating in a contemporary system with different geographical system boundaries. Thereafter, future energy market scenarios for Europe are investigated to explore possible future outcomes. This study concludes that both the heat production system and the energy market conditions affect the system emission effects of using excess heat in district heating. Industrial excess heat in district heating can be beneficial even if it leads to reduced local electricity production when unused biomass can be used to replace fossil fuels. It is recommended that a strengthened EU policy should encourage the use of biomass where it has the most favorable effects from a systems perspective to ensure emission reductions when industrial excess heat is used in district heating.

Allocative and strategic effects of privacy enhancement in smart grids

Local energy markets are a promising approach for automatic and efficient matching of renewable energy with household demand in smart grids. Therefore, such markets can help to improve power system reliability and at the same reduce emissions. However, to participate in such markets, customers need to disclose private consumption data. A number of studies show that such data records may reveal a broad range of personal, sensitive information on the inhabitants. Privacy-enhancement mechanisms can be applied to preserve the privacy of individuals to modify the data reported to the market. Yet, these mechanisms can lower allocative efficiency and alter theoretical properties of the market mechanism.In this paper, we characterize both theoretically and numerically the effect of privacy mechanisms applied in a local energy market scenario. Our model considers demand side flexibility as well as energy storage systems. Furthermore, we allow for a free specification of the desired privacy enhancement level. We show that under certain natural assumptions market mechanisms retain in-expectation incentive compatibility despite the presence of privacy enhancement. Our numerical analysis based on real-world data shows that the welfare impact of privacy enhancement mechanisms is limited. Furthermore, energy storage can mitigate this efficiency loss to a large extent.