Optimal Operation Strategy Research Articles

Optimal operation strategy of wind-hydrogen integrated energy system based on NSGA-II algorithm

In order to improve the economy of the multi energy system and the efficiency of energy utilization, the research adopts the non dominated sorting genetic algorithms II (NSGA-II) to expand the population space. The elite strategy is introduced to improve the intelligent algorithm, and then the diversity of the population is retained to improve the optimization accuracy of the algorithm. In addition, the adaptive operator is introduced to improve the NSGA-II algorithm to improve the global search efficiency. The performance test of fast non dominated sorting genetic algorithm shows that the improved algorithm using elite strategy has better performance in coverage index, diversity index and convergence index. For example, in terms of convergence index, the improved NSGA-II algorithm has improved 0.0159, 0.822, 0.0243 and 0.0171 in four ZDT test functions. On the energy optimization operation for the integration of wind and hydrogen, the improved NSGA-II algorithm has obtained lower cost, with a total configuration cost of 606 million yuan, while the total system configuration cost corresponding to the unimproved NSGA-II algorithm is 624 million yuan, so the total system cost after the algorithm improvement has decreased by 18 million yuan. Therefore, this method has a better economy and higher energy efficiency.

A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty

A microgrid energy management system (EMS) with several generation and storage units is crucial in attaining stable and reliable operation. Optimal scheduling of energy resources in EMS becomes arduous due to uncertainty in the forecasting of intermittent renewable sources, electricity pricing, and load demand. However, with the demand response (DR) approaches the operational benefits in the EMS framework can be maximized. In order to improve the cost-effectiveness of the microgrid, a novel day-ahead energy management strategy is proposed for optimal energy allocation of the distributed generators with environmental consideration. An incentive load control-based demand response program is developed to improve the operational results. The forecasting uncertainties are handled using probability-based Hong’s 2 m approximation method. The suggested approach uses a metaheuristic genetic algorithm (GA) to solve the constrained convex problem in determining optimal load shifting. Incentive pricing is developed to adapt to the demand shifting for the benefit of the customers and utility operators. Two case studies with grid-connected and islanded modes are studied to assess the strategy. Results indicate that the proposed technique reduces the overall cost fitness by 12.28% and 18.91% in the two cases, respectively. The consistency in operational parameters with popular methods confirms the effectiveness and robustness of the method for day-ahead energy management.

Resilience enhancement strategy of multi-energy coupling distribution network considering movable energy storage equipment

With the deepening of energy transformation, multi-energy coupling has become an effective method to alleviate the energy gap and improve the energy utilization rate. As a bridge connecting users and the power system, the distribution network is becoming multi-energy coupling as well. Therefore, to ensure a stable supply of power, heat, and other energy sources, it is of great significance to study the resilience improvement of multi-energy coupling distribution networks. This paper proposes a strategy for improving the resilience of multi-energy coupling distribution networks considering movable energy storage equipment (MESS) configuration. Firstly, the multi-energy coupling distribution network model is established, and the mathematical model of each piece of equipment is established considering the coupling of the power grid and heat grid. Secondly, based on the loss of load probability (LOLP), considering the importance of the load, it is expanded to the loss of important load probability as an index to evaluate the resilience of distribution networks. Thirdly, a distribution network resilience improvement model based on the configuration and operation of the MESS is established, and the optimal configuration capacity and operation strategy of the MESS is solved by taking the minimum operation cost as the objective function, i.e., the configuration cost of the MESS, the load shedding cost and the cost of wind and PV abandonment. Finally, based on the IEEE-33 node distribution network, a multi-energy coupling distribution network model is built, which verifies that the method proposed in this paper can effectively reduce the total cost and improve the resilience of the multi-energy coupling distribution network.

Exploration on the Operation Status and Optimization Strategy of Networked Teaching of Physical Education Curriculum Based on AI Algorithm

Artificial Intelligence (AI) and networked teaching have made great progress in recent years, especially the application of AI algorithms in teaching, which has been relatively mature. The online teaching of physical education (PE) courses is an important practice of networked teaching, but many colleges and universities ignore the importance of AI when carrying out such teaching work. Many colleges and universities have not played a good role in AI in teaching, which leads to inefficient PE teaching and low learning enthusiasm of students.

Thermo-economic optimization of an enhanced geothermal system (EGS) based on machine learning and differential evolution algorithms

Hot dry rock (HDR) is considered as a promising low-carbon alternative to fossil fuels, but the remaining economic challenges are leading to its unsuccessful exploitation. Therefore, incorporating economic indicators into consideration is essential to optimize an enhanced geothermal system (EGS). However, the conventional optimization approaches based on numerical simulations are time-consuming and a global optimal operation strategy is hard to determine. In this study, an optimization framework based on Artificial Neural Network (ANN) and Differential Evolution (DE) is proposed with considering a levelized cost of electricity (LCOE) being an economic performance indicator to optimize a three-horizontal-well EGS in the Qiabuqia field. Specifically, four different ANN models are constructed to predict different geothermal productivities to substitute numerical models. Based on these ANN models, a DE optimization process is conducted to determine an optimal LCOE under two field operating constraints, followed by a performance comparison between the resulting optimal geothermal system and 2,150 randomly created cases using a numerical simulator. The results show that these ANN models all achieve a coefficient of determination R2 higher than 0.996, demonstrating their predictive abilities and potential as surrogate models. The determined optimal parameters configuration brings a promising LCOE of 0.0376 $/kWh which is around 50 % of a local electricity cost, and this is the lowest LCOE among all random cases. Importantly, the proposed framework can significantly save operation time by 36,000 times compared with the numerical simulation method. The proposed method provides a valuable reference for the geothermal system studied, and it can also be effectively applied to other energy systems, thereby facilitating their optimal development.

Optimization of site utility systems for renewable energy integration

Considerable attention has been paid to the electrified energy supply based on renewable energy for achieving carbon-neutrality. A systematic and integrated approach is required to identify optimal operating strategies for the integration of electrified energy sources with conventional utility systems and to understand the techno-economic impact of using renewable energy on industrial energy management. Renewable-integrated industrial utility systems are modeled, which is optimized to investigate economic trade-off between capital investment, fuel consumption, power generation, and CO2 emission tax. Sensitivity of key design parameters is examined with the optimization framework, which allows to gain conceptual understanding on the economic impact of CO2 emission tax and prices of renewable electricity on site-wide heat and power management. Compared to the utility system based on the combustion of fossil fuels only, the operating cost of renewable-integrated system in the case study can be reduced about 14% in the operating cost and 9% in the capital cost through the strategic import of 20 MWe renewable electricity. The presented case studies fully illustrate economic impacts related to the implementation of renewable electricity to industrial utility systems and demonstrate the benefit of process-integrated optimization for renewable energy integration in practice.

Model predictive control of sun-coupled innovative heat pumps: a comparison of economic and environmental optimizations

Background: Heating and cooling in buildings represents a significant amount of the energy demand in the EU, but the market penetration of renewable solutions is still marginal. The SunHorizon project aims at proving the viability and benefits of innovative coupling between heat pumps and various advanced solar panels. Methods: This study focuses on the optimal operation strategies of a technological package composed of hybrid photovoltaic thermal (PVT) panels, a gas driven heat pump and a hot water storage tank. In this work, a model predictive control is developed, based on mixed integer linear programming (MILP) optimization. This model uses innovative elements compared to traditional model predictive control (MPC), with environmental indicators for the electricity grid accounting for imports, co-simulation with TRNSYS using the transmission control protocol (TCP) and modelling of inter-seasonal storage for long and short-term decisions. The usual minimization of costs is compared to two new optimization approaches, which aims to minimize greenhouse gas (GHG) emissions and maximizing renewable use. Results and conclusions: The results of the optimization of costs and GHG emissions show that gains can be found within the variations in time series related to the electricity grid, but the overall operation strategies remain similar. Optimization of renewable share and self-consumption is another path for control strategy, but with less economic and environmental performance.

A Bi-Level Optimization Model for Virtual Power Plant Membership Selection Considering Load Time Series

In order to improve the level of new energy consumption and reduce the dependence of the power system on traditional fossil energy, this paper proposed a bi-level optimization model for virtual power plant member selection by means of coordination and complementarity among different power sources, aiming at optimizing system economy and clean energy consumption capacity and combining it with the time sequence of load power consumption. The method comprises the following steps: (1) The processing load, wind power, and photovoltaic data by using ordered clustering to reflect the time sequence correlation between new energy and load and (2) uses a double-layer optimization model, wherein the upper layer calculates the capacity configuration of thermal power and energy storage units in a virtual power plant and selects the new energy units to participate in dispatching by considering the utility coefficient of the new energy units and the environmental benefit of the thermal power units. The Latin hypercube sampling (LHS) method was used to generate a large number of subsequences and the mixed integer linear programming (MILP) algorithm was used to calculate the optimal operation scheme of the system. The simulation results showed that by reducing the combination of subsequences between units and establishing a reasonable unit capacity allocation model, the average daily VPP revenue increased by RMB 12,806 and the proportion of new energy generation increased by 1.8% on average, which verified the correctness of the proposed method.

Research on potential user identification and optimal planning of the multiple time scale cloud‐based location sharing energy storage

AbstractCloud energy storage is considered a promising application in future power systems. It focuses on optimally leveraging the capacity of centralized large‐scale energy storage compared with the requirements of small‐scale localized users. In this paper, to satisfy the small‐ and medium‐scale timely energy storage requirement from localized users, the concept of the cloud‐based location sharing energy storage is proposed. The modular mobile energy storage system is flexibly configured and deployed at different sites to fulfil the long‐term seasonally dynamic transformer capacity increment and short‐term daily energy arbitrage based on economic values. To optimize the overall incomes of the energy storage investment, a two‐step user potential identification algorithm is proposed to discover the most valuable users at different time scales from the regional power usage profile. Then, the number/capacity optimal planning algorithm is proposed to optimally share the mobile energy storage system among users seasonally and total profits are further increased through daily energy arbitrage at less valuable seasons. Finally, the structure and dispatching strategy of the cloud‐based location sharing energy storage management system is illustrated. Case study results prove that the proposed identification algorithm can excavate the most valuable users at different time scales and the optimal planning and operation strategy is able to guarantee the overall income benefits.

Development of an artificial intelligence-based framework for biogas generation from a micro anaerobic digestion plant

Despite the advantages of the Anaerobic Digestion (AD) technology for organic waste management, low system performance in biogas production negatively affects the wide spread of this technology. This paper develops a new artificial intelligence-based framework to predict and optimise the biogas generated from a micro-AD plant. The framework comprises some main steps including data collection and imputation, recurrent neural network/ Non-Linear Autoregressive Exogenous (NARX) model, shuffled frog leaping algorithm (SFLA) optimisation model and sensitivity analysis. The suggested framework was demonstrated by its application on a real micro-AD plant in London. The NARX model was developed for predicting yielded biogas based on the feeding data over preceding days in which their lag times were fine-tuned using the SFLA. The optimal daily feeding pattern to obtain maximum biogas generation was determined using the SFLA. The results show that the developed framework can improve the productivity of biogas in optimal operation strategy by 43 % compared to business as usual and the average biogas produced can raise from 3.26 to 4.34 m3/day. The optimal feeding pattern during a four-day cycle is to feed over the last two days and thereby reducing the operational costs related to the labour for feeding the plant in the first two days. The results of the sensitivity analysis show the optimised biogas generation is strongly influenced by the content of oats and catering waste as well as the optimal allocated day for adding feed to the main digester compared to other feed variables e.g., added water and soaked liner.

An Optimal Method of Energy Management for Regional Energy System with a Shared Energy Storage

The regional energy system (RES) is a system that consumes multiple forms of energy in the region and achieves coordinated and efficient utilization of energy resources. The RES is composed of multiple micro energy systems (MESs); however, due to the mismatch of energy resources and different energy consumption within each MES, a large amount of clean energy is wasted, and each MES has to acquire extra energy. This significantly increases operation costs and contributes to environmental pollution. One of the promising ways to solve this problem is to deploy an energy storage system in the RES, which can make use of its advantages to transfer energy in space-time and fulfill the demand for loads in different periods, and conduct unified energy management for each MES in the RES. Nevertheless, a large number of users are deterred by the high investment in energy storage devices. A shared energy storage system (SESS) can allow multi-MESs to share one energy storage system, and meet the energy storage needs of different systems, to reduce the capital investment of energy storage systems and realize efficient consumption of clean energy. Taking multiple MESs as the object, this paper proposes a model and collaborative optimal strategy of energy management for the RES to accomplish high utilization of clean energy, environmental friendliness, and economy. First, the paper analyzes the internal energy supply characteristics of the RES and develops a model of the RES with an SESS. Then, the paper poses the management concept of load integration and unified energy distribution by using the operational information of each subsystem. An optimal operation strategy is established to minimize daily operation costs and achieve economic, environmentally friendly, and efficient operation of the RES. Third, by setting up scenarios such as no energy storage system and an independent energy storage system (IESS) of each MES and SESS, a case of a science and education park in Guangzhou, China, is illustrated for experiments. Numerical experiment results show that with an SESS built by the investor in the RES and applying the mentioned energy management strategy, the utilization of clean energy can be 100%, the operation costs can be reduced by up to 9.78%, the pollutant emission can be reduced by 3.92%, and the peak-to-valley difference can be decreased by 20.03%. Finally, the influence of energy storage service fees and electricity tariffs on daily operation costs is discussed, and the operation suggestions of the SESS are proposed. It validates the effectiveness of the proposed strategy.

An integrated decision support method for strategic planning and tactical management of regional biomass power plants under uncertainties

In the context of global climate change and energy security pressures, biomass power generation has been regarded as an effective way to supplement the energy supply, reduce fossil fuel consumption, and alleviate environmental pollutants. This paper presents an integrated modeling framework to address the regional biomass power generation planning to facilitate biomass utilization in developing countries. By combining the geographic information system, multi-criteria decision-making approach, as well as the type-2 fuzzy inexact programming method, the proposed integrated framework could assist the optimal decision-making for the specific site and size selection of biomass power plants in the whole region as well as the optimal operation strategies under deep uncertainties. The proposed modeling framework is applied to the particular context of Henan, an agricultural province in China with great biomass potential. The results suggested that biomass power plants with a total capacity of 2.4 GW could be established, which could satisfy 6% of local electricity consumption and avoid 18.51 Mt of carbon emissions. The sensitivity analysis of the main economic parameters indicated that lower investment and operation costs were essential to stimulate the utilization of local biomass energy. A higher feed-in tariff is not encouraged due to the limited incentive effect but is a heavy burden for the government. Furthermore, the optimal strategies for biomass power generation planning under uncertainties could be suggested for different decision makers with various risk preferences to achieve a tradeoff between system cost and risk.

Multi-agent deep reinforcement learning based distributed control architecture for interconnected multi-energy microgrid energy management and optimization

Environmental and climate change concerns are pushing the rapid development of new energy resources (DERs). The Energy Internet (EI), with the power-sharing functionality introduced by energy routers (ERs), offers an appealing alternative for DER systems. However, previous centralized control schemes for EI systems that follow a top-down architecture are unreliable for future power systems. This study proposes a distributed control scheme for bottom-up EI architecture. Second, model-based distributed control methods are not sufficiently flexible to deal with the complex uncertainties associated with multi-energy demands and DERs. A novel model-free/data-driven multiagent deep reinforcement learning (MADRL) method is proposed to learn the optimal operation strategy for the bottom-layer microgrid (MG) cluster. Unlike existing single-agent deep reinforcement learning methods that rely on homogeneous MG settings, the proposed MADRL adopts a form of decentralized execution, in which agents operate independently to meet local customized energy demands while preserving privacy. Third, an attention mechanism is added to the centralized critic, which can effectively accelerate the learning speed. Considering the bottom-layer power exchange request and the predicted electricity price, model predictive control of the upper layer determines the optimal power dispatching between the ERs and main grid. Simulations with other alternatives demonstrate the effectiveness of the proposed control scheme.

A Compound Coordinated Optimal Operation Strategy of Day-Ahead-Rolling-Realtime in Integrated Energy System

Aiming at the impact of the uncertainty of source load on the optimal scheduling in an integrated energy system (IES), in this paper, based on hybrid resolution modeling and hybrid instruction cycle scheduling technology, three time scales of day-ahead, intra-day rolling and real-time feedback optimization scheduling models are established, respectively, with the objectives of the economic optimal daily operation of the system, the minimum sum of the operation cost of energy purchase and wind curtailment penalty cost in the rolling control time domain, and the minimum adjustment amount of equipment output power. Then, the chaotic gravitational search algorithm (CGSA) is used to solve the problem, and the composite coordination optimization operation strategy of IES with mixed time scales based on CGSA is proposed. In the example, the comparison between the multi-timescale scheduling plan and the actual output, the comparison of the system scheduling results under different strategies and the comparison of different optimization algorithms show that the proposed optimization operation strategy is beneficial to optimize the energy flow distribution, reduce the system operation cost, improve the IES economy and optimization speed.

Optimized Energy-Flexible Operation Strategies of Air Conditioning Systems in Production Environments

Rising energy prices and regulatory requirements are forcing both large manufacturing companies and small and medium-sized enterprises to minimize costs and emissions associated with energy supply. To operate the considered systems more efficiently overall, numerous energy efficiency measures have already been successfully implemented in recent years. This is particularly the case for the cross-industry air conditioning systems that are the focus of this paper. However, due to the growing share of renewable energies and the resulting fluctuating electricity prices and CO2 emissions factors, energy flexibility measures are becoming increasingly relevant. In this context, flexibility measures allow industrial system operators to benefit from these fluctuations, primarily using energy storages and optimized operation strategies. However, the implementation of energy flexibility measures requires high data availability, a good understanding of the system, and flexibility in production planning. To be able to effectively use the existing energy flexibility potentials, air conditioning systems are examined, that contain widely used cross-section technologies. In this paper, a method to enable optimized energy-flexible operation of industrial air conditioning systems is presented. A common feature of manufacturing sites is a similar topology of the thermal supply infrastructure across industries. The complex overall system is divided into several subsystems, e.g., cooling supply system, ventilation system and the production hall. Production processes typically release waste heat into the indoor air of the production hall, which then must be cooled. By accounting for thermal and electrical output and optimizing the operation strategy using mathematical optimization, both operating costs and CO2 emissions associated with energy provision can be significantly reduced. The presented use case reveals that operational cost savings of up to 18 % and a reduction of CO2 emission associated with energy supply of up to 16 % can be realized by optimizing the cooling supply system on its own.

Energy Management and Capacity Optimization of Photovoltaic, Energy Storage System, Flexible Building Power System Considering Combined Benefit

Building structures themselves are one of the key areas of urban energy consumption, therefore, are a major source of greenhouse gas emissions. With this understood, the carbon trading market is gradually expanding to the building sector to control greenhouse gas emissions. Hence, to balance the interests of the environment and the building users, this paper proposes an optimal operation scheme for the photovoltaic, energy storage system, and flexible building power system (PEFB), considering the combined benefit of building. Based on the model of conventional photovoltaic (PV) and energy storage system (ESS), the mathematical optimization model of the system is proposed by taking the combined benefit of the building to the economy, society, and environment as the optimization objective, taking the near-zero energy consumption and carbon emission limitation of the building as the main constraints. The optimized operation strategy in this paper can give optimal results by making a trade-off between the users’ costs and the combined benefits of the building. The efficiency and effectiveness of the proposed methods are verified by simulated experiments.

Control-oriented modeling and passivity analysis of thermal dynamics in a multi-producer district heating system

Climate change and geopolitics have led to the conception of plans for reducing greenhouse gas emissions and improving the sustainability of existing fossil-based energy systems. In this respect, district heating has been identified as an indispensable player for its potential to integrate seamlessly environmentally-friendly heat sources. To improve the efficiency of these district heating systems, optimal operation schemes can be devised and enforced through control systems. To this end, we present a control-oriented nonlinear ODE-based model of temperature dynamics in a multi-producer district heating system. The model features a modular design and comprises the thermal dynamics of heat exchangers of producers and consumers interconnected by a distribution network of meshed topology. Then, we establish passivity properties and zero-state detectability for the modeled temperature dynamics that could be exploited for controller design and solving constrained optimization problems.

Tri-Level Energy Management Strategy for Optimal Operation of a Grid Tied Microgrid Considering Demand Response and Uncertainties

Tri-Level Energy Management Strategy for Optimal Operation of a Grid Tied Microgrid Considering Demand Response and Uncertainties

Avoiding PV-Induced Overvoltage through Grid-Connected Batteries Using Model Predictive Control

Abstract The problem of temporary overvoltage when integrating large renewable power plants into the existing grid is considered. A grid-connected battery energy storage system with an advanced predictive control algorithm is proposed to reduce the overvoltage in time periods of excessive renewable production. An approximative grid model is developed using system identification methods on regular operation data. A model predictive control algorithm utilizing predictions of load and generation determines the optimal operation strategy of the battery. A comparison in simulation between a reference case with PID control and the proposed algorithm shows a large reduction of the time spent in overvoltage conditions, especially in the case of consecutive days of high renewables production. The results suggest that energy storages combined with a predictive control strategy can effectively alleviate the overvoltage problem in renewables integration. Especially in the case when available storage capacity is comparatively low, a uniform overvoltage reduction can be realized. The method can potentially increase the PV host capacity of current grids.

Optimization of Operations of Air Purifiers for Control of Indoor PM2.5 Using BIM and CFD

Prevention of PM2.5 is attracting increasing attention all over the world as PM2.5 can cause and exacerbate respiratory diseases. Compared with conventional natural ventilation and mechanical ventilation, air purifiers with particular filters are more efficient in the control of indoor PM2.5. Currently, there are limited studies on the operations of portable indoor air purifiers. To improve the efficiency of an air purifier in control of PM2.5, an optimal operational strategy is needed. This paper proposes a framework for the optimization of operations of air purifiers for control of indoor PM2.5 based on building information modeling (BIM) and computational fluid dynamics (CFD). A case study is conducted to validate the proposed optimization framework and the results are discussed. It can be shown from the results that the best location of the air purifier can increase efficiency by up to 33% compared with other locations.