System Optimization Model Research Articles

China's future forest carbon sequestration potential under different management scenarios

Enhancing forest carbon storage and carbon sequestration capacity is crucial for achieving carbon neutrality. Scientific forest management can maintain a high level of carbon sequestration capacity in forests, and considering the carbon pool of wood products can extend the time of carbon fixation. However, current predictions of large-scale forest carbon storage and carbon sequestration capacity have overlooked changes in forest carbon absorption with forest age and the buffering effect of wood product carbon pools in the carbon release process. In this paper, we used the Forest Simulation and Optimization System model (FSOS) to analyze the wood supply and carbon sequestration capacities of different management scenarios based on data from the Forest Resources Inventory (2014–2018). The results showed that China's forests could export significant amounts of timber in the future; According to the 9th forest survey report, China's forests produce only 88 million m3 yr-1. Among them, the harvested objects were mainly planted forests, and if all forests (planted forests and natural forests) are involved in forest management planning, the maximum sustainable annual wood supply will reach 286 million m3 yr-1. Moreover, due to the current large proportion of younger forests in China, 358 million m3 of annual wood supply will be achievable in the future (as of the year 2039). Forest management can increase the carbon sequestration capacities in forest ecosystems as well as the wood supply compared to the no management options. In summary, the carbon sequestration potential of unmanaged forests is limited. Appropriate forest management can increase the carbon sequestration potential of forests. The substitution of carbon emission reduction of wood products and bioenergy can also greatly reduce the pressure to achieve carbon neutral strategies.

Exploring European decarbonisation pathways in the Power Decisions Game

BackgroundArticle 12 of the Paris Agreement summons the signing parties to co-operate in improving the education of their citizens on climate change and related matters. The article thereby acknowledges the importance of citizens’ support and understanding of climate change and needed measures to fight climate change. This work aims to inform European citizens on how climate change-related policies affect the power sector in Europe. For this purpose, a serious game, based on sound principles of energy systems analysis, has been developed to allow players to explore how key policy decisions affect capacity mix, investment needs, and electricity costs.ResultsThe game is based on more than 1700 scenarios run through an open-source and accessible, yet technologically detailed, myopic energy system optimisation model for the electricity supply in the EU27 + 3. The game allows the user to take the role of a decision-maker and make decisions in 2020, 2030, and 2040 regarding the usage of CCS, biomass imports, cross-border electricity transmission and the pace of emission reductions. The user is then presented with economic, social, and environmental impacts of these choices. These impacts are, for example, measured and illustrated in the development of accumulated CO2 emissions per capita, levelised cost of electricity, and investment need per citizen.ConclusionThe Power Decisions Game provides a first-of-its-kind open-source infrastructure that allows non-modellers to explore the impact of key decisions and preferences on the design of the future European power system. Furthermore, it provides insights on the consequences of short-sighted decision making. The game can be used to facilitate policy-science discussions.

Research on the Coordinated Trading Mechanism of Demand-Side Resources and Shared Energy Storage Based on a System Optimization Model

With the development of the economy and society, the importance of a secure and stable electricity supply continues to increase. However, the power grid is facing the test of excess installed capacity, the waste of renewable energy, and a low comprehensive utilization rate. This problem stems from the inconsistent peak–valley differences between power production and consumption, and the lack of clear electricity price signals, which disrupts the safe and stable operation of the power market. This paper combines the interactive transactions among clean energy power generation companies, users, and energy storage, explores how the system optimization model can be reflected in the power market through regulatory measures, and formulates the optimal output scheme of the system under the constraints of clean energy power generation forecast data, user base load forecast data, demand-side resource regulation ability, and energy storage system regulation ability to achieve the goals of comprehensive clean energy power consumption and minimum cost for users. A comprehensive analysis of the proposed model was conducted using actual data from a certain province in China, the results show that the consumption of clean energy will increase by 3% to full consumption and the total cost of users will be 32% lower than that of time-of-use (TOU) power prices, which proves the potential of the proposed joint optimization model in absorbing clean energy and the effectiveness of the market mechanism.

Thermodynamic and life cycle assessment analysis of polymer-containing oily sludge supercritical water gasification system combined with Organic Rankine Cycle

With the development of polymer flooding technology, polymer-containing oily sludge (PCOS) has become a major pollutant on offshore oil platforms. Supercritical water gasification system is an efficient and pollution-free technology compared to conventional PCOS treatment. However, the system model for PCOS has not yet been established and the theoretical model for system optimization is insufficient. In this work, a novel auto-thermal system is established based on supercritical water gasification technology for the treatment of PCOS. Through the thermodynamic analysis, an optimization plan is concluded and used for the establishment of a modified system with Organic Rankine Cycle. Results show that the two-stage reactor reduces the gasification reaction temperature from 652 °C to 502 °C, and the Organic Rankine Cycle system using Isopropanol recover 9.1 % chemical exergy of feedstock with the optimal parameters (superheat, evaporation temperature and circulating pressure). The energy and exergy efficiency of the modified system has increased by 52.4 % and 42.4 % compared to the original system. Besides, a life cycle assessment is adopted for the environment analysis. The results show that the increasing gasification temperature and PCOS slurry concentration can reduce system unit exergy loss. Global Warming Potential reaching the minimum value of 26.1 at 65 wt%.

Constrained Reinforcement Learning for Fair and Environmentally Efficient Traffic Signal Controllers

Traffic signal controller (TSC) has a crucial role in managing traffic flow in urban areas. Recently, reinforcement learning models have received a great attention for TSC with promising results. However, these RL-TSC models still need to be improved for real-world deployment due to limited exploration of different performance metrics such as fair traffic scheduling or air quality impact. In this work, we introduce a constrained multi-objective RL model that minimizes multiple constrained objectives while achieving a higher expected reward. Furthermore, our proposed RL strategy integrates the peak and average constraint models to the RL problem formulation with maximum entropy off-policy models. We applied this strategy to a single TSC and a network of TSCs. As part of this constrained RL-TSC formulation, we discuss fairness and air quality parameters as constraints for the close-loop control system optimization model at TSCs called FAirLight . Our experimental analysis shows that the proposed FAirLight achieves a good traffic flow performance in terms of average waiting time while being fair and environmentally friendly. Our method outperforms the baseline models and allows a more comprehensive view of RL-TSC regarding its applicability to the real world.

Operation Optimization of Regional Integrated Energy Systems with Hydrogen by Considering Demand Response and Green Certificate–Carbon Emission Trading Mechanisms

Amidst the growing imperative to address carbon emissions, aiming to improve energy utilization efficiency, optimize equipment operation flexibility, and further reduce costs and carbon emissions of regional integrated energy systems (RIESs), this paper proposes a low-carbon economic operation strategy for RIESs. Firstly, on the energy supply side, energy conversion devices are utilized to enhance multi-energy complementary capabilities. Then, an integrated demand response model is established on the demand side to smooth the load curve. Finally, consideration is given to the RIES’s participation in the green certificate–carbon trading market to reduce system carbon emissions. With the objective of minimizing the sum of system operating costs and green certificate–carbon trading costs, an integrated energy system optimization model that considers electricity, gas, heat, and cold coupling is established, and the CPLEX solver toolbox is used for model solving. The results show that the coordinated optimization of supply and demand sides of regional integrated energy systems while considering multi-energy coupling and complementarity effectively reduces carbon emissions while further enhancing the economic efficiency of system operations.

Development, validation and demonstration of a new Modelica pit thermal energy storage model for system simulation and optimization

Space heating applications account for a high share of global greenhouse gas emissions. To increase the renewable share of heat generation, seasonal thermal energy storage (STES) can be used to make thermal energy from fluctuating renewable sources available in times of high demand. A popular STES technology is pit thermal energy storage (PTES), where heat is stored underground, using water as a storage medium. To evaluate the use of PTES in an energy system, easily adaptable, publicly accessible and tool independent models are needed. In this paper, we improve an existing PTES model developed in the Modelica modeling language. The model is cross-compared with a more detailed and previously validated COMSOL model, considering different amounts of insulation, showing a deviation of 2–13% in the observed annual charged and discharged amount of heat. The results indicate that the presented model is well suited for early design stage and an exemplary case study is performed to demonstrate its applicability in a system context. Dimensions of system components are optimized for the levelized cost of heat (LCOH), both with and without subsidies, highlighting the importance of subsidies for the transition towards climate friendly heating solutions, as the gas boiler use is reduced from 47.6% to 2.7%.

Flexible copper: Exploring capacity-based energy demand flexibility in the industry

Different forms of flexibility can help in balancing variable generation. This work focuses on industrial demand-side flexibility applied in copper production, which is expected to grow for the build-out of green technologies.This study assesses the potential of capacity-based energy demand flexibility (over-sizing production processes) in an industry embedded in fully renewable energy systems. For this, an optimization model for multi-vector energy systems planning is extended so that it also includes the sizing and operation of a production process. A case study is presented for copper production, with greenfield results until 2050.Results show that flexibility at the concentration and refining stages belongs to the cost-optimal system design, at least over the next decade. At current costs, the potential cost savings in the energy system for the production process through capacity-based demand flexibility range from 5 % to 12 %, depending on the technology scenario. These potential savings are expected to decrease over time if cost reductions of renewable energy supply and storage technologies materialize. Technology scenarios considering seawater pumped-hydro energy storage yield lower costs over the entire projected period.

Research on low-carbon optimization methods for regional energy interconnection systems considering multi-energy coupling characteristics

Abstract With the increasing application of multi-energy cogeneration, distributed renewable power generation, and multi-energy storage in various multi-energy systems, their efficient collaborative optimization has become the key to further improving emissions. This article utilizes energy hubs to integrate energy equipment with multi-energy coupling and multi-source supply characteristics and constructs a regional interconnected energy system that considers multi-energy coupling characteristics. A multi-objective optimization model for the above-mentioned energy system is established, including economic, low-carbon, and safe aspects such as operating costs, process emissions, and operational safety, and the raccoon optimization algorithm is applied to solve the model. The experimental results show that compared with classical scheduling algorithms, the proposed method can effectively reduce carbon emissions at a certain operating cost.

Optimal configuration of hybrid hydrogen‐to‐power system for power systems with high wind energy penetration

AbstractHydrogen energy storage plays an important role in improving the operation efficiency and reliability of power systems with high wind energy penetration. Hydrogen to power (HtP) system is the key link of hydrogen applications. However, the single HtP equipment is limited in power output range and efficiency. Hybrid HtP system is an important scheme to realize the performance complementary. A wider power output range can enrich the application scenarios of hydrogen energy storage and provide flexible and reliable energy scheduling in larger and more complex energy systems. In this study, first, a power system including traditional units, wind power generation and hybrid HtP system is established. Second, a bi‐layer hybrid HtP system optimal configuration model considering planning and operation is constructed. Then, a bi‐layer optimal configuration method based on the improved PSO algorithm is proposed. Finally, the optimization model and its solution method are applied to IEEE RTS‐96. The discussions based on the optimization results show that the hybrid HtP system has significant contributions in optimizing the output cost of traditional units, improving the utilization of wind energy, and reducing load shedding losses.

The long term price elastic demand of hydrogen – A multi-model analysis for Germany

Hydrogen and its derivatives are important components to achieve climate policy goals, especially in terms of greenhouse gas neutrality. There is an ongoing controversial debate about the applications in which hydrogen and its derivatives should be used and to what extent. Typically, the estimation of hydrogen demand relies on scenario-based analyses with varying underlying assumptions and targets. This study establishes a new framework consisting of existing energy system simulation and optimisation models in order to assess the long-term price-elastic demand of hydrogen. The aim of this work is to shift towards an analysis of the hydrogen demand that is primarily driven by its price. This is done for the case of Germany because of the expected high hydrogen demand for the years 2025–2045. 15 wholesale price pathways were established, with final prices in 2045 between 56 €/MWh and 182 €/MWh. The results suggest that – if climate targets are to be achieved - even with high hydrogen prices (252 €/MWh in 2030 and 182 €/MWh in 2045) a significant hydrogen demand in the industry sector and the energy conversion sector is expected to emerge (318 TWh). Furthermore, the energy conversion sector has a large share of price sensitive hydrogen demand and therefore its demand strongly increases with lower prices. The road transportation sector will only play a small role in terms of hydrogen demand, if prices are low. In the decentralised heating for buildings no relevant demand will be seen over the considered price ranges, whereas the centralised supply of heat via heat grids increases as prices fall.

A multi-stage optimization of battery electric bus transit with battery degradation

Battery electric buses (BEBs) are appealing to transit operators for their elevated comfort, low noise, and zero tailpipe emissions. However, the degradation of BEB batteries over time challenges their performance and necessitates infrastructure adjustments. This study develops a generic multi-stage optimization model for BEB systems. The model addresses the dynamic influence of BEB battery degradation rates on the optimal BEB system configuration and operation, including component sizing, charging infrastructure allocation, BEB charging schedule, and battery replacement throughout the BEB usage lifecycle. A surrogate model-based space mapping (SMSM) algorithm is employed to address the inherent nonlinearity of incorporating battery degradation rates within the developed model. The model is tested on a real-world, multi-hub transit network, and the results highlight significant implications of battery degradation on the optimal spatiotemporal allocation of charging infrastructure, charging schedules, and battery replacement decisions throughout the 12-year BEB usage lifecycle. Sensitivity analysis highlights the influence of operational conditions on total system cost, indicating a 16.3 % increase in cost with a 50 % rise in both energy consumption rates and time-of-use (ToU) tariffs. Overall, the proposed model is a valuable decision-making tool for transit operators navigating BEB transit system planning.

Research on Dynamic Reactive Power Cost Optimization in Power Systems with DFIG Wind Farms

As the power market system gradually perfects, the increasingly fierce competition not only drives industry development but also brings new challenges. Reactive power optimization is crucial for maintaining stable power grid operation and improving energy efficiency. However, the implementation of plant–grid separation policies has kept optimization costs high, affecting the profit distribution between power generation companies and grid companies. Therefore, researching how to effectively reduce reactive power optimization costs, both technically and strategically, is not only vital for the economic operation of the power system but also key to balancing interests among all parties and promoting the healthy development of the power market. Initially, the study analyzes and compares the characteristic curves of synchronous generators and DFIGs, establishes a reactive power pricing model for generators, and considering the randomness and volatility of wind energy, establishes a DFIG reactive power pricing model. The objective functions aimed to minimize the cost of reactive power purchased by generators, the price of active power network losses, the total deviation of node voltages, and the depreciation costs of discrete variable actions, thereby establishing a dynamic reactive power optimization model for power systems including doubly-fed wind farms. By introducing Logistic chaotic mapping, the CSA is improved by using the highly stochastic characteristics of chaotic systems, which is known as the Chaotic Cuckooing Algorithm. Meanwhile, the basic cuckoo search algorithm was improved in terms of adaptive adjustment strategies and global convergence guidance strategies, resulting in an enhanced cuckoo search algorithm to solve the established dynamic reactive power optimization model, improving global search capability and convergence speed. Finally, using the IEEE 30-bus system as an example and applying the improved chaotic cuckoo search algorithm for solution, simulation results show that the proposed reactive power optimization model and method can reduce reactive power costs and the number of discrete device actions, demonstrating effectiveness and adaptability. When the improved chaotic cuckoo algorithm is applied to optimize the objective function, the optimization result is better than 7.26% compared to the standard cuckoo search algorithm, and it is also improved compared to both the PSO algorithm and the GWO algorithm.

Improved restricted Boltzmann machine-based optimization model for the network security system in cloud environment

This paper introduces a novel approach to enhancing computer network security through deep learning and cloud technologies, focusing on combating insider DDoS attacks in cloud computing. By employing the IRBM architecture and a trust-oriented secure cloud framework, the work utilizes parameter tuning in RBM through the Gradient-Based Optimization (GBO) algorithm resulting in the proposed IRBM-GBO model. This model significantly outperforms SVM, SC-CNSP-ML, and RBM in terms of latency and Packet Delivery Ratio (PDR), showing 21.67%, 14.55%, and 9.62% improvements in latency, and 10.23%, 5.43%, and 12.79% in PDR, respectively. The research highlights the potential of combining soft computing with deep learning and cloud technologies to identify cyberattacks by emphasising improvements in latency, Quality of Service (QoS), throughput, and PDR across various cybersecurity datasets.

Identifying a selection mechanism of distribution channel for the supply chain: The barriers to the application of web 3.0

The application of Web 3.0 will bring unprecedented changes to the supply chain, where many industries are seeing significant cost reductions. However, due to the requirement of stability for supply chain, sometimes the total cost of supply chain cannot decrease effectively under Web 2.0, which may hinder the application of Web 3.0. Following the application from Web 2.0 to Web 3.0, this paper discusses the selection of distribution channel whose cost is under user stickiness and the degree of digitalization. The system optimum model, which is commonly used in dynamic traffic allocation, is applied to solve the distribution channel selection problem. The results implies that when the degree of digitalization is low, the distribution channel cost first decreases and then increases as user stickiness increases. This trend of channel cost illustrates the price induction on user stickiness initially, and gradually adjust the price to be higher when user stickiness makes channel transformation costly. Customers with unclear user stickiness perceptions and weak channel control can easily be trapped in the transition from Web 2.0 to Web 3.0. Distributors will also be forced to accept high product costs due to the price induction from product distribution channels.

A single-objective optimization model for power systems with the introduction of line-heavy load rate index constraints

The increasing penetration of new energy sources in new power systems has exacerbated the volatility and complexity of power system currents. Grid planning is facing higher challenges. Established grid planning models usually use the upper and lower limits of grid transmission capacity as constraints, but this may lead to long-term heavy load operation of certain lines and increase system risks. Therefore, this paper designs a line-heavy load rate constraint and establishes a single-objective optimization model for grid planning based on this constraint. Example analyses verify the effectiveness of adding line-heavy load rate constraints in planning and the reasonableness of the single-objective optimization model.

Understanding the role and design space of demand sinks in low-carbon power systems

As the availability of weather-dependent, zero marginal cost resources such as wind and solar power increases, a variety of flexible electricity loads, or ‘demand sinks’, could be deployed to use intermittently available low-cost electricity to produce valuable outputs. This study provides a general framework to evaluate any potential demand sink technology and understand its viability to be deployed cost-effectively in low-carbon power systems. We use an electricity system optimization model to assess 98 discrete combinations of capital costs and output values that collectively span the range of feasible characteristics of potential demand sink technologies. We find that candidates like hydrogen electrolysis, direct air capture, and flexible electric heating can all achieve significant installed capacity (>10% of system peak load) if lower capital costs are reached in the future. Demand sink technologies significantly increase installed wind and solar capacity while not significantly affecting battery storage, firm generating capacity, or the average cost of electricity.

A cloud-edge cooperative scheduling model and its optimization method for regional multi-energy systems

In the process of multi-energy system optimal scheduling, due to the high data processing requirements of the multi-energy devices and loads and the complexity of the operating states of the multi-energy devices, the scheduling optimization of the system is to some extent more difficult. To address this problem, this paper proposes a regional multi-energy system optimal scheduling model based on the theory of cloud-edge collaboration. First, based on intelligent data sensors, a cloud-edge cooperative scheduling framework of the regional multi-energy system is constructed. Then, the physical model of operating state data of multi-energy system equipment and the allocation mechanism of system scheduling tasks are studied. With the cloud service application layer and the edge computing layer as the upper and lower optimization scheduling layers, the double-layer optimization scheduling model of the regional multi-energy system is established. The objectives of the model are optimal scheduling cost and minimum delay of scheduling data transmission. The multi-objective whale optimization algorithm is used to solve the model. Finally, a simulation model is built for verification. The simulation results show that the scheduling model established in this paper can effectively improve the scheduling data processing capability and improve the economy of regional multi-energy system scheduling.

Seasonal pumped hydropower storage role in responding to climate change impacts on the Brazilian electrical sector

Since Brazil's major energy resources are renewable and directly related to climate factors, it is among the countries most likely of being affected by climate change. Given Brazil's high hydropower storage capacity and the strong seasonal patterns of its renewable resources, introducing Seasonal Pumped Hydropower Storage (SPHS) can help mitigate these challenges. To this end, a methodology is proposed that links the dynamic system-optimization model – MESSAGEix - to regional climate model simulations, called the Brazilian Electricity System MESSAGEix Model (BESMM). This model, with its detailed hydropower representation, is capable of integrating data from three climate change scenarios with the country's energy system. Climate change introduces a new dimension to this approach, as there is evidence of increasing the seasonal imbalance of variable renewable resources in Brazil. BESMM results suggest that SPHS can play a fundamental role in achieving a 100 % renewable matrix by 2100 in RCP 2.6 scenario, as well as enhancing the renewable energy endowment in scenarios RCP 4.5 and RCP 8.5. A reduction of up to 68 % of CO2 emissions is predicted in scenarios incorporating SPHS, compared to scenarios without SPHS.

Is District Heating a cost-effective solution to decarbonise Irish buildings?

Achieving decarbonisation of the heating sector is a major challenge, especially in Ireland, which has the lowest proportion of renewable heat in Europe. In economically pursuing ambitious and equitable climate targets, Ireland’s Climate Action Plan 2023 set a goal of reaching 2.5 TWh of district heating by 2030, starting from a limited existing capacity. This pioneering study explores supply sources and heat density demand cohorts to develop a model to understand district heating in a low-deployment region, using Ireland as a case study. We also conduct a sensitivity analysis considering different connection rates.Our research demonstrates that district heating is pivotal in achieving optimal decarbonisation pathways. Incorporating district heating reduces marginal CO2 emission prices by 13%–25%. Additionally, with feasible growth limits applied, total savings for the energy system are anticipated to reach €17.2 billion by 2050. This reduction of emissions in the building sector, facilitated by district heating, allows for greater emissions allowances in other sectors and reduces reliance on electricity while meeting climate targets. These insights highlight the need for spatial energy policies and deepen our understanding of integrating district heating within an energy system optimisation model.