Improved time representation model for the simultaneous energy supply and demand management in microgrids

• A unified continuous-time framework is developed for enterprise-wide scheduling and planning optimization • Continuous-time representation eliminates the time-scale inconsistencies between planning and scheduling • Three representative continuous-time formulations are systematically evaluated in terms of optimality and computational efficiency • The unit-slot formulation achieves superior efficiency and solution quality Inconsistent continuous-time and discrete-time representations have been a big challenge in achieving the enterprise-wide optimum for integrated crude scheduling and refinery planning problems. This paper proposes a unified continuous-time representation for solving the integrated enterprise-wide optimization problem. Instead of implementing continuous-time slots for the crude scheduling subproblem and discrete-time slots for the refinery planning subproblem in a hybrid way, continuous-time slots are applied to both subproblems and synchronized via the crude distillation units. Such a proposed modeling framework provides a more flexible and consistent time formulation to the integrated problem. Furthermore, three typical continuous-time representations for crude scheduling, namely, unit-slot time representation, event-based time representation, and multi-operation sequence time representation, are investigated with cases adapted from practical complexes. Compared to the traditional integrated problem based on a hybrid continuous-time and discrete-time representation, the proposed unified model framework reveals a better optimization performance. Higher-quality solutions can be obtained for both crude scheduling and planning operations. Moreover, among the three time representations, the model based on the unit-slot time outperforms the other two models. This work provides a promising alternative modeling strategy for achieving enterprise-wide optimality in the petrochemical industry.

In considering of high proportion of renewable energy supply in 2050, the accelerating of energy consumption gross, source and environment can affect the energy system restrict affection are stronger. Add wind and solar to electricity energy with large amount of energy source exploitation. The energy source amount per person is lower. Considering the renewable energy amount and supply, primary energy storage and structure problem is standing out. Before the wide spread of renewable energy, Using the high-carbon energy in China can pollute seriously. Chinese energy supply and demand problem is research key point. This paper researches Chinese energy supply and demand pattern system and evaluation methodology, gives out the inner and outer influencing elements. And evaluate Chinese energy supply and demand pattern from energy gross, structure, distribution and transportation. Use energy supply synthesize radar comparison chart in certain time period. From energy security, economy, clean and efficiency, analyze the benefit comparisons of Chinese energy supply and demand pattern. This energy supply and demand pattern model will give one certain theoretical analysis and practice reference to the further high proportion of renewable energy.

The choice of a suitable time representation plays a major role in designing a computationally efficient planning/scheduling framework. For the time representation to be effective, it needs to capture the nuances of the actual process, while ensuring that the resultant model is tractable and computationally efficient. In this paper, a novel time representation scheme, titled hybrid time discretization, which combines both continuous- and discrete-time representations in a single framework, is proposed and used to solve a complex multiproduct maritime inventory routing problem (MIRP) with undedicated compartments. Several continuous- and discrete-time-based frameworks have been proposed to solve the MIRP over the years. While single-/multigrid discretized frameworks do efficiently capture the time-sensitive nuances of the problem (such as active/inactive jetty hours and hourly/daily variations in the demands), they struggle to solve larger problem instances to the required optimality gap in a reasonable time. This is primarily due to the large disparities among the timings of different task components in the model. On the other hand, continuous-time representation can significantly reduce the problem size and computational effort; however, modeling the entire problem using the continuous-time-based framework could have problems in representing some time-sensitive intricacies in the demands and jetty/port activities. To allay the above-mentioned drawbacks, in this paper, a novel hybrid time representation is proposed, which combines a continuous-time representation with a multigrid discrete-time framework. The proposed novel framework is cast in a rolling horizon strategy and tested on various scenarios generated for the above-mentioned maritime transportation problem, which are shown to be significantly more complex than those presented in the literature. The proposed MILP framework is able to solve most of the test cases to the required optimality in a reasonable time without the need for heuristic or meta-heuristic assists. The proposed hybrid time representation could find significant utility in other lateral applications such as batch scheduling and inventory routing problems.

Machine learning is applied in almost every field of knowledge in recent years. Renewable energy domain now extensively uses machine learning to provide farmers and grid operators with proper and timely information about solar irradiance prediction for future planning. Connecting grids with solar energy systems has brought unprecedented shift in the power sector. Nevertheless, the intervention poses challenges including intermittency that affects the overall energy supply and demand management. Our proposed solution intends to make prediction models using Machine Learning techniques (Linear Regression, Regression Trees and Support Vector Machine) which uses the past weather data including the parameters such as temperature, wind speed, atmospheric pressure, and humidity and predicts solar irradiance for the future. The model can help the grid operators for improved supply and demand management.

The energy transition to a decarbonized energy scenario leads toward distributed energy resources in which end users can both generate and consume renewable electricity. As a result, several challenges arise in terms of decentralized energy resource management and grid reliability. With microgrids, the cooperation of distributed energy resources is improved, and with peer-to-peer energy exchange and demand response programs, better energy allocation and flexible management of consumption loads according to the needs of supply systems are achieved. However, effective peer-to-peer energy allocation and flexible demand management in microgrids require the development of market structures, pricing mechanisms, and demand response strategies enabled by a reliable communication system. In this field, blockchain offers a decentralized communication tool for energy transactions that can provide transparency, security, and immutability. Therefore, this paper provides a comprehensive review of key factors for peer-to-peer energy trading and flexible energy demand management in blockchain-enabled microgrids. The goal is to provide guidelines on the basic components that are useful in ensuring efficient operation of microgrids. Finally, using a holistic view of technology adoption as a tool for peer-to-peer communication in microgrids, this paper reviews projects aimed at implementing blockchain in energy trading and flexible demand management.

Hybrid distributed finite-time neurodynamic optimization of electric vehicle charging schemes management in microgrid considering time-varying factors

Thermal Energy Storage (TES) system has emerged as a promising solution of energy demand and supply management, which stores excess thermal energy and releases it when energy demand is high, making it an efficient and cost-effective energy storage solution when combined with renewable energy sources, such as solar and wind power. This study aimed to evaluate the thermal performance of TES units using Computational Fluid Dynamics (CFD) simulations in the ANSYS CFX software package. After comparing the heat storage capacity of conventional Phase Change Material (PCM) and iron oxide/paraffin wax composite ($2 \%$) using industrial residual water, temperature distribution plots and heat flux data were generated in simulations for both cases. Addition of iron oxide nanoparticles significantly improved the heat absorption performance of TES units. Both materials initially exhibited a higher heat absorption rate, which gradually decreased over time. CFD data analysis revealed that iron oxide/paraffin wax material enhanced heat absorption performance by up to $1.3 \%$, which demonstrated the potential of iron oxide nanoparticles in improving the efficiency of TES system and highlighted the advantages of TES system combined with renewable energy sources. By improving heat absorption properties, the incorporation of iron oxide nanoparticles had the potential to increase the lifespan of TES units and significantly reduced maintenance and replacement expenses. This breakthrough, along with the cost savings and energy efficiency offered by TES technology, may encourage its widespread application, thus reducing reliance on fossil fuels and promoting sustainable energy practices.

In order to realize the coordinated operation between distributed resources and the grid, the village can be equivalent to an energy supply system, to give full play to the flexibility of distributed resources and improve energy utilization. Establish a resource aggregation model based on the scenario where the energy supply and consumption system participate in grid regulation and control, this paper proposes a dynamic aggregation equivalence method. Firstly, cluster the power generation resources in the countryside, and conduct unified control of the scattered and single-point power generating units with small capacity; secondly, establish the day-ahead equivalent model of the multi-type energy supply and use the system formed by the cluster, construct an optimized model to obtain the power range and climbing range of the energy supply and use system, and obtain the cost curve of the energy supply and use system; then, according to the parameters of the day-ahead equivalent model, real-time operation information and prediction information of distributed generator set, the diurnal equivalent parameters of energy supply system are modified; finally, an example is given to verify the effectiveness of the proposed method.

The future role of CO2 capture in the electricity sector

This paper presents a stochastic optimization framework for microgrid (MG) energy management, integrating electric bicycle (E-Bike) and electric vehicle (EV) charging stations with a green certificate market (GCM) to enhance sustainability and economic efficiency. Uncertainties in renewable energy generation (solar and wind) and load demand are modeled using the Two-Point Estimation Method (TPEM), enabling robust handling of variability. A novel metaheuristic algorithm, Mountaineering Team-Based Optimization (MTBO), is developed to solve a three-objective optimization problem: minimizing operational costs, minimizing emissions, and maximizing GCM revenue. MTBO is benchmarked against particle swarm optimization, achieving a 21.6% reduction in operational costs and a 13.12% reduction in emissions in deterministic scenarios. Three cases are analyzed: (I) no mobile storage, (II) deterministic mobile storage, and (III) stochastic management. In case II, integrating mobile storage (EVs and E-Bikes) with V2G capabilities reduces operational costs by 18.6% and emissions by 10.9% compared to case I. Case III, incorporating stochastic management, further lowers costs by 1.5% and by 19.8% relative to case I, but increases demand response costs by 7.5% and reduces GCM revenue by 38.9% due to renewable fluctuations. These results highlight the practical benefits of combining E-Bike and EV mobile storage with GCM trading in microgrids, demonstrating MTBO's superior exploration and exploitation capabilities for high-dimensional, uncertainty-aware energy scheduling.

The necessity of fulfilling customer requests leads to challenges for the production departments of manufacturing enterprises because a high product variety has to be managed at the same time with a high speed of order changes. If this variability cannot be compensated with sufficient process flexibility this will lead to an instable situation. Therefore a methodology would be very helpful which is able to analyze the necessary flexibility demand. The analysis of flexibility demand is crucial for the design of production systems. The objective must be to define the degree of flexibility that it is able to fulfill market demands. In the contribution a concept is presented for the flexibility oriented design of production systems which is derived from the management of energy supply and power plants. In order to work out the energy production in energy supply systems the demand is classified in certain zones: basic, middle and peak load. For each level a different production strategy is applied. Our central idea is to classify the demand in production regarding its variety in different classes in order to enable a differentiated capacity budgeting and to derive the optimal degree of flexibility.

Scenario analysis for integrated water resources planning and management under uncertainty in the Zayandehrud river basin

Evacuations are massive operations that create heavy travel demand on road networks some of which are experiencing major congestions even with regular traffic demand. Congestion in traffic networks during evacuations, can be eased either by supply or demand management actions. This study focuses on modeling demand management strategies of optimal departure time, optimal destination choice and optimal zone evacuation scheduling (also known as staggered evacuation) under a given fixed evacuation time assumption. The analytical models are developed for a system optimal dynamic traffic assignment problem, so that their characteristics can be studied to produce insights to be used for large-scale solution algorithms. While the first two strategies were represented in a linear programming (LP) model, evacuation zone scheduling problem inevitable included integers and resulted in a mixed integer LP (MILP) one. The dual of the LP produced an optimal assignment principle, and the nature of the MILP formulations revealed clues about more efficient heuristics. The discussed properties of the models are also supported via numerical results from a hypothetical network example.

The destruction of Ukraine's power infrastructure due to military actions exacerbates the problem of sustainable household energy supply. Frequent power, gas, and water outages lead to network overloads, rising tariffs, and decreased energy security. The vulnerability of traditional centralized systems highlights the need to explore alternative power supply approaches for the residential sector, particularly through the development of prosumerism, which involves active household participation in energy generation. This paper examines possible energy strategies for seven groups of typical private households utilizing different energy sources, including renewables. Methodological approaches are proposed for selecting economically optimal energy solutions, considering natural and climatic conditions, energy resource prices, the feasibility of using green technologies and energy efficiency measures, as well as state energy policy. Specifically, the potential of solar and wind power plants, battery storage systems, and diesel generators as means of autonomous energy supply for the residential sector is analyzed. The advantages and drawbacks of these technologies are assessed in the context of possible short- and long-term disconnections from the main power grid. The study results indicate that an effective household energy strategy should be based not only on minimizing economic costs but also on increasing energy independence and resilience to crises. Optimal economic decisions must take into account all energy-related home expenditures (including costs for heating, cooking, water heating, lighting, and economic losses caused by power outages) as well as potential revenues (such as savings achieved through self-produced energy consumption and income from selling energy surpluses). Key factors influencing household decision-making include government support, the availability of investment resources, and the implementation of economic mechanisms to stimulate the use of renewable energy sources. The proposed methodological approaches contribute to strengthening the country’s overall energy security and to developing effective models of sustainable energy supply for households.

In models of demand and supply, consumer price sensitivity affects both the sales of a good through price, and the price that is set by producers and retailers. The relationship between the dependent variables (e.g., demand and price) and the common parameters (e.g., price sensitivity) is typically non-linear, especially when heterogeneity is present. In this paper, we develop a Bayesian method to address the computational challenge of estimating simultaneous demand and supply models that can be applied to both the analysis of household panel data and aggregated demand data. The method is developed within the context of a heterogeneous discrete choice model coupled with pricing equations derived from either specific competitive structures, or linear equations of the kind used in instrumental variable estimation, and applied to a scanner panel dataset of light beer purchases. Our analysis indicates that incorporating heterogeneity into the demand model all but eliminates the bias in the price parameter due to the endogeneity of price. The analysis also supports the use of a full information analysis.