Renewable Energy Penetration Research Articles

SWOT Analysis of the Benefits of Hydropower Energy in Four Archipelagos

Increasing energy production through renewable sources is a challenge for islands. This paper investigates the potential of hydropower as a renewable energy source for islands in the Macaronesia region, which includes the Azores, Madeira, Canary Islands, and Cape Verde. Ecological transition towards renewable energy sources is crucial for these islands due to their current dependence on imported fossil fuels and their remoteness. The methodology used in this paper combines a SWOT analysis with a review of relevant literature. The SWOT analysis evaluates the Strengths, Weaknesses, Opportunities, and Threats associated with hydropower development on each island. The results show that each island has unique characteristics that influence its hydropower potential. The Azores has existing mini-hydropower plants and opportunities for pumped storage systems due to its rainfall and volcanic features. Madeira also utilizes hydropower, including the world's first underground pumped storage plant (UPHS) in Socorridos. However, limitations exist due to the mountainous terrain and competition for water resources. The Canary Islands showcase the success story of El Hierro Island, which significantly increased renewable energy penetration through a wind farm and pumped storage hydropower system. The topography and lack of rainfall on Cape Verde make the development of hydropower a significant challenge and, as a result, the focus has shifted to wind power. The study concludes that hydropower can play a significant role in the ecological transition of these islands. However, careful planning and consideration of environmental factors are necessary to maximize the benefits and minimize the potential drawbacks. The paper emphasizes the importance of island-specific assessments and exploring opportunities for pumped storage systems. Doi: 10.28991/CEJ-2024-010-07-019 Full Text: PDF

Improved Newton-Raphson method with simplified Jacobian matrix and optimized iteration rate for power flow calculation of power system

With high penetration of renewable energy and novel loads connected to the distribution network, the voltage fluctuation becomes more severe and frequent, which may cause over- and under-voltage. The distribution system operator should calculate the power flow and validate the state to optimize the distribution network. Power flow calculation is the solution to the multivariate nonlinear problem, and the Newton-Raphson method is an effective algorithm for solving nonlinear problems. However, calculating the Jacobian matrix is a crucial process of the Newton-Raphson method, which is time-consuming. Therefore, this paper proposed an improved Newton-Raphson method, which simplifies and decreases the iterations of the calculation process of the Jacobian matrix to improve the calculation rate. To verify the effectiveness of the proposed method, the power flow of the IEEE 33-node power distribution system is calculated by the improved Newton-Raphson method and the conventional Newton-Raphson method.

A Design of Economically Feasible Hybrid Energy System with Renewable Energy Ratio Priority

The reduction of fossil fuels which produce CO2 emission that damage the environment, can be done by implementing renewable energy-based power generations, such as solar and wind. This research designs a hybrid energy system by optimizing the use of existing diesel generators through the integration of renewable energy sources, such as solar photovoltaic and micro wind turbine, and is equipped with an energy storage system. This research uses HOMER Pro software to determine the optimal capacity of hybrid system components, and to calculate the cost of energy (CoE). Furthermore, the hybrid system configuration is analyzed by applying several objectives. The objectives of the hybrid system design are to prioritize a maximum renewable energy penetration ratio within permitted annual capacity shortage and with the CoE lower than the existing CoE. The research results show that the proposed hybrid energy system can provide a renewable energy penetration ratio of 57.1% with CoE of IDR 3,510/kWh.

Urban emissions and land use efficiency scenarios for avoiding increments of global warming

Urban areas have key roles in climate mitigation, including increasing renewable energy penetration in smart energy systems. The potential of urban areas in limiting global warming, however, is not quantified. This research work develops a new framework to analyse urban emissions and land use efficiency scenarios with coupling to the transient climate response to cumulative carbon dioxide emissions. Two new parameters that quantify contributions to and avoidance of increments of global warming from urban areas are formulated and applied to twelve scenario combinations. The results for 465 urban areas indicate that a green-growth scenario with lower ambition in reducing urban emissions can lead up to a 0.13 °C additional increment of global warming in 2050 based on the best estimate of the transient climate response to cumulative carbon dioxide emissions. Those of the most ambitious urban emissions scenario with green-growth and renewable energy are limited to 0.06 °C. When combined with the most ambitious land use efficiency scenario for 135 urban areas, it is possible to avoid more than double or 2.09 times additional increments of global warming in comparison to other integrated urban scenarios. The mean and 5–95th percentile range of the results are compared based on 10,000 Monte Carlo simulations. Informing on the urgency to ensure ambitious mitigation action in urban areas can support a climate future that remains within critical thresholds and tipping elements of the planet.

Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid

Power scheduling is an NP-hard optimization problem that demands a delicate equilibrium between economic costs and environmental emissions. In response to the growing concern for climate change, global environmental policies prioritize decarbonizing the electricity sector by integrating renewable energies (REs) into power grids. While this integration brings economic and environmental benefits, the intermittency of REs amplifies the uncertainty and complexity of power scheduling. Existing optimization approaches often grapple with a limited number of units, overlook critical parameters, and disregard the intermittency of REs. To address these limitations, this article introduces a robust and scalable optimization algorithm for renewable integrated power scheduling based on reinforcement learning (RL). In this proposed methodology, the power scheduling problem is decomposed into Markov decision processes (MDPs) within a multi-agent simulation environment. The simulated MDPs are used to train a deep reinforcement learning (DRL) model for solving the optimization. The validity and effectiveness of the proposed method are validated across various test systems, encompassing single-to tri-objective problems with 10–100 generating units. The findings consistently demonstrate the superior performance of the proposed DRL algorithm compared to existing methods, such as multi-agent immune system-based evolutionary priority list (MAI-EPL), binary real-coded genetic algorithm (BRCGA), teaching learning-based optimization (TLBO), quasi-oppositional teaching learning-based algorithm (QOTLBO), hybrid genetic-imperialist competitive algorithm (HGICA), three-stage priority list (TSPL), real-coded grey wolf optimization (RCGWO), multi-objective evolutionary algorithm based on decomposition (MOEAD), and non-dominated sorting algorithms (NSGA-II and NSGA-III). Regarding the experimental results, it is important to highlight the importance of integrating RESs into larger power systems. In a 10-unit system with 2.81 % RE penetration, reductions of 3.42 %, 4.03 %, and 3.10 % were observed in costs, CO2 emissions, and SO2 emissions, respectively. Similarly, in a 100-unit system with a RE penetration rate of only 0.28 %, reductions of 3.75 % in cost, 4.42 % in CO2, and 3.34 % in SO2 were observed. These findings emphasize the effectiveness of RES integration, even at lower penetration rates, in larger-scale power systems.

Thermo-economic analysis of a pumped thermal energy storage combining cooling, heating and power system coupled with photovoltaic thermal collector: Exploration of low-grade thermal energy storage

As renewable energy penetration grows, traditional power systems face significant challenges due to their intermittency and volatility. Pumped thermal energy storage (PTES) is a potential energy storage technology that has a low specific cost and geographical restriction. In this paper, a PTES system which is coupled with solar photovoltaic thermal (PVT) collectors is proposed to satisfy the demand for cooling, heating and electricity supply, and achieve energy cascade utilization. An ejector refrigeration cycle and the turbine extraction heating are accompanied with a PTES system. Thermodynamic analysis of the system is performed by evaluating the influence of key parameters on system performances for 1 MW power output. The economic analysis adopted levelized cost of energy storage (LCOS), which mainly includes investment costs, operation and maintenance costs and system depreciation. The results show that the maximum amplification of roundtrip efficiency with PVT is 12.04 % compared to the system without PVT. Moreover, the maximum average power generation benefit of PVT is 4.85 %. The increase in cold energy storage tank temperature can effectively improve the roundtrip efficiency of the system. The lower cold energy storage tank temperature and higher hot energy storage tank temperature have a negative impact on system thermal efficiency (ηthermal) but benefits for LCOS. Multi-objective optimization is carried out to obtain the optimal design performance that ηthermal and LCOS are 51.06 % and 0.533$/kWh respectively.

Distributed optimal scheduling for virtual power plant with high penetration of renewable energy

The virtual power plants are aggregations of distributed generators, grid-connected devices in user side. The operation of virtual power plants affects the economic benefits and environmental benefits. However, due to the stochastic and fluctuating nature of power generation from renewable energy sources, the optimal scheduling problem for the virtual power plant containing high-dimensional random variables is difficult to solve. The conic power flow constraints are considered in the virtual power plant day-ahead optimal scheduling models to maximize the revenue by virtue of optimizing the flexible resources such as energy storage system and interruptible load. To quantify the uncertainties in the optimization problem, this paper proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to be solved directly by traditional centralized method. By temporally decoupling the charging and discharging model of energy storage systems, a distributed optimization algorithm is proposed based on multi-temporal power flow decoupling optimization model. The simulation results show that the proposed distributed optimization algorithm has high accuracy and good convergence, the virtual power plant can achieve day-ahead optimal scheduling and effectively promote renewable energy accommodation under the security and reliability of the grid.

Health status perception of oil-immersed power transformers considering wind power uncertainty

The power transformer is the most important and critical component in the power grid of the electrical system. Its safe and stable operation is of great significance for the reliable transmission of renewable energy generation and the reliable power supply to end users. With the continuous development of new types of power systems, the load of the power system undergoes drastic changes, resulting in increased volatility and instability, which leads to issues such as overload, harmonics, and short circuits in transformers. Therefore, in order to accurately assess the operating status of transformers and promptly identify any existing conditions, a method is proposed in this paper. This method uses an optimal cloud entropy parameter calculation method and a variable weighting method to optimize the gray-cloud evidence model. Through a novel multi-source information fusion method, the results of various test items are fused to obtain the state awareness results. Meanwhile, different evaluation indicators are selected for different levels of renewable energy penetration and compared with other methods. The results are validated through examples, demonstrating that the method proposed in this paper can accurately reflect the operating status of transformers and has good scalability.

Investigating the influence of erratic grid on stationary battery energy storage technologies in hybrid power systems: Techno-environ-economic perspectives

Several obstacles impede renewable energy penetration into the global energy sector. Amongst the apparent challenges which require credible research attention, is the selection of appropriate energy storage technologies in the context of intermittent renewable resources and frequent grid outages. Given these, the present study investigated the technical, economic, and environmental effects of an erratic national grid on four distinct battery technologies in hybrid wind, solar, and diesel energy systems. The study was conducted at Baze University Abuja, Nigeria using the Hybrid Optimization Model for Electric Renewables software. The battery types considered are vanadium redox flow battery (VRB), lead-acid battery, nickel-iron battery, and lithium-ion battery (LIB). The VRB-based hybrid energy systems demonstrated superior performances in meeting the electricity demands of the university at the lowest net present cost, levelized cost of energy, and carbon dioxide emissions of $6,328,003.00, $0.0722/kWh, and 21,754 kg/year respectively. This battery technology is characterized by storage depletion, throughput, and losses of 796 kWh/year, 406,570 kWh/year, and 182,757 kWh/year respectively. The sensitivity analysis showed that the frequency and duration of main grid outages affect the optimal systems’ economics, component sizes, battery energy losses, battery energy storage depletion, and renewable energy penetration.

Optimal hybrid resonant current controller for microgrids connected to an unbalanced IEEE test distribution network

Microgrids (MGs) based on renewable energies have emerged as a proficient strategy for tackling power quality issues in conventional distribution networks. Nonetheless, MG systems require a suitable control scheme to supply energy optimally towards the electrical grid. This paper presents an innovative framework for designing hybrid Proportional-Resonant (PR) controllers with Linear Quadratic Regulators (LQR), PR+LQR, which merge relevant properties of PR and LQR controllers. This method simultaneously determines the MG control parameters and the current unbalanced factor generated at the distribution network. We select the traditional IEEE 13-bus test feeder network and place two MGs at strategic locations to validate our approach. Moreover, we use the Grey Wolf Optimizer (GWO) to find control parameters through a reliable fitness function that leads to high-performance microgrids. Finally, we conceive several tests to assess the efficacy of GWO for tuning the hybrid controller and compare the resulting data across distinct realistic operation conditions representing power quality events. So, we choose four case studies considering different renewable energy penetration indexes and power factors and evaluate the effects of the MGs over the distribution grid. We also compare the proposed hybrid PR+LQR controller against closely-related alternatives from the literature and validate its robustness and stability through the disk margin approach and the Nyquist criterion. Our numerical simulations prove that hybrid controllers driven by GWO are highly reliable strategies, yielding an average unbalanced current reduction of 30.03%.

Energy storage systems impact on Egypt's future energy mix with high renewable energy penetration: A long-term analysis

High renewable energy penetration targets cannot be achieved without more reliance on energy storage technologies. This study provides a long-term techno-economic analysis for the energy mix of Egypt until 2050. That is with considering various types of energy storage including pumped hydropower, electro-chemical (Redox flow battery) and (Li-Ion battery), and hydrogen energy. Since 80 % renewable energy generation from all primary energy sources was targeted by 2050, six different scenarios were studied by using the PLEXOS energy model. The results showed that the capacity of pumped storage hydropower (PSHP) is expected to reach 21.0 GW, contributing to almost 3.7 % from total energy supply by 2050. The electrolyzers' capacity for Hydrogen Energy Storage System (HESS) is expected to reach 15.0 GW, producing 20.69 TWh of Hydrogen energy by 2050. Besides that, the Levelized Cost of Energy storage (LCOS) of (PSHP) is expected to reach 189.8 (US$/MWh) compared with 60.83 (US$/MWh) in case of (HESS) by 2050. On the other hand, it is anticipated that the capacity of both redox flow and Lithium-Ion batteries will be 5.1 GW and 3.0 GW, respectively by 2050.

Capacity optimization and multimode operation analysis of electric vehicle charging system powered by grid, solar and geothermal energy

The high penetration of renewable energy in electric vehicle (EV) charging system is critical for the EV industrial promotion and carbon neutrality in transportation area. In this study, an EV charging station powered by grid, solar photovoltaic (PV) and geothermal energy from abandoned wells for a highway is proposed together with novel multi-mode operation strategies. Based on the EV charging loads predicted from highway traffic information, NSGA-II algorithm is used for capacity optimization, and two control strategies (i.e., geothermal power multistep output regulation, and geothermal power multistep output + flexible control in charging load regulation) are proposed to achieve the multimode operation. The matching relationship between energy supply and charging load under the proposed and conventional control strategies are compared. The results show that the proposed control strategies allow the optimization of geothermal operation by avoiding the frequent ON/OFF of geothermal power block and increasing its full-generation duration. As a result, the corresponding shares of renewable energy can have 14.8 % and 20.2 % increases, and operation costs are reduced by 14.4 % and 20.9 %, respectively. This study helps to promote the rational configuration of renewable energy-powered charging system for EV and enhance the low-carbon transportation infrastructure.

Revealing electricity conversion mechanism of a cascade energy storage system

With the increasing penetration of renewable energy in the power system, it is necessary to develop large-scale and long-duration energy storage technologies. Deploying pump stations between adjacent cascade hydropower plants to form a cascade energy storage system (CESS) is a promising way to accommodate large-scale renewable energy sources, yet the mechanism how renewable curtailment is converted to hydroelectricity is still unclear. In this paper, we aim to clarify this mechanism by evaluating the CESS's long-term operational efficiency and changes compared to the cascade hydropower system. First, operational features and principle of the CESS was outlined. Then, long-term operations of the CESS and cascade hydropower system were, respectively, optimized using a simulation-based optimization framework. Finally, the operational efficiency of the CESS, defined as a ratio of reduced renewable energy curtailment to increased hydropower production, was calculated based on the above two optimized operation results. China's Longyangxia-Laxiwa CESS was selected as a case study. Results show that: (1) long-term operational efficiency of the CESS reached 81.6 %, i.e., 81.6 % of the curtailed renewable could be converted to hydroelectricity; (2) the increase in hydropower production was mainly attributed to the upper hydropower plant, accounting for about 96.5 % of the total increment; and (3) the increase in generation flow was the main factor to impact the increased hydropower production of the upper plant, although it could decrease the hydropower plant's hydraulic head.

Parameter Design of a Self-Generated Power Current Transformer of an Intelligent Miniature Circuit Breaker Based on COMSOL

With the deep penetration of renewable energy and power electronic equipment, the overcurrent protection of an intelligent miniature circuit breaker faces new challenges. The electronic controller of an intelligent miniature circuit breaker is typically powered by the bus current rather than the phase voltage to ensure a robust overcurrent protection response under all conditions, including severe short-circuit faults. So, the performance of the current transformer serving as an energy harvesting unit and the corresponding direct current to direct current convention circuit is one of the critical issues due to the limited volume of an intelligent miniature circuit breaker. In this research, a finite element model of a current transformer for an intelligent miniature circuit breaker is constructed by COMSOL to evaluate the impact of the core material, the core size, and the number of coil turns on the energy harvesting capability of the current transformer. Meanwhile, the relationship between the output of the power supply and its design parameters is investigated by circuit simulation. As a result, a novel type of current transformer is proposed based on well-designed parameters. Finally, experimental tests have been conducted to verify the hysteresis characteristics, output characteristics, and energy harvesting effect. The results demonstrate that the hysteresis properties of the transformer align with the simulation results. The power supply can work with a minimum current of 8 amperes, which is 23.08% better than before.

Multi-objective optimization and evaluation of the building-integrated photovoltaic/thermal-energy pile ground source heat pump system

Building integrated photovoltaic thermal (BIPV/T)-energy pile ground source heat pump (GSHP) system effectively maintains the soil thermal balance and improves the photovoltaic efficiency by recovering the waste solar heat from the BIPV/T collector to charge the ground. However, due to the strict carbon emission restriction and economic consideration for system application, multi-objective optimization should be implemented for the system design to further enhance its overall performance. Therefore, a complete optimization framework is proposed in this paper, which is based on the NSGA-II algorithm and functions by coupling TRNSYS and jEPlus+EA. Using the proposed optimization frame, a comprehensive optimization is conducted for the system, which optimizes five main design variables and adopts the total power consumption (TPC), life cycle climate performance (LCCP), and life cycle cost (LCC) to reflect the system's energetic, environmental, and economic performances. Seven optimization cases are included in the optimization project to identify design schemes that meet all possible proposed performance requirements for the system application. The optimized results show that the system optimization improves its performance greatly compared to the design condition, with a range of 38.03% ∼ 50.05% for the TPC, 24.52% ∼ 25.86% for LCCP, and 56.74% ∼ 61.66% for LCC. The energetic, environmental and economic performances of the system can be maximized by minimizing the TPC to 357.29 MWh in Case 1, LCCP to 596.15 tCO2 in Case 2, and LCC to 17.03 × 104 CNY in Case 3. By breaking down the objective functions, it is found that the heat pump unit holds paramount importance in all aspects of system performance, and the BIPV/T collector proves advantageous in enhancing the system's long-term performance due to its massive electricity generation. Accordingly, one important finding is that it is advisable to incorporate as many BIPV/T collectors as feasible while maintaining soil heat balance. The optimal energy pile number varies among the seven optimization cases, resulting in different heating capacities for the heat pump unit. Research outcomes in this work offer references for designing the energy-pile GSHP system to enhance the penetration of renewable energy into buildings.

Dynamic Robust Optimization Method Based on Two-Stage Evaluation and Its Application in Optimal Scheduling of Integrated Energy System

As an emerging energy allocation method, shared energy storage devices play an important role in modern power systems. At the same time, with the continuous improvement in renewable energy penetration, modern power systems are facing more uncertainties from the source side. Therefore, a robust optimization algorithm that considers both shared energy storage devices and source-side uncertainty is needed. Responding to the above issues, this paper first establishes an optimal model of a regional integrated energy system with shared energy storage. Secondly, the uncertainty problem is transformed into a dynamic optimization problem with time-varying parameters, and a modified robust optimization over time algorithm combined with scenario analysis is proposed to solve such optimization problems. Finally, an optimal scheduling objective function with the lowest operating cost of the system as the optimization objective is established. In the experimental part, this paper first establishes a dynamic benchmark test function to verify the validity of proposed method. Secondly, the multi-mode actual verification of the proposed algorithm is carried out through a regional integrated energy system. The simulation results show that the modified robust optimization over time (ROOT) algorithm could find solutions with better robustness in the same dynamic environment based on the two-stage evaluation strategy. Compared with the existing algorithms, the average fitness and survival time of the robust solution obtained by the modified ROOT algorithm are increased by 94.41% and 179.78%. At the same time, the operating cost of the system is reduced by 11.65% by using the combined optimization scheduling method proposed in this paper.

Dispatch Optimization Scheme for High Renewable Energy Penetration Using an Artificial Intelligence Model

Dispatch Optimization Scheme for High Renewable Energy Penetration Using an Artificial Intelligence Model

Industrial Park low-carbon energy system planning framework: Heat pump based energy conjugation between industry and buildings

The accelerating urbanization, rapid industrial development, and excessive consumption of fossil fuels pose survival challenges such as energy depletion and environmental degradation for humanity. In the context of industrial park development, constructing a low-carbon energy system, increasing the proportion of renewable energy, enhancing energy-level matching, and utilizing heat pumps for deep waste heat recovery are effective approaches to reduce fossil energy consumption and alleviate environmental pressures. Addressing issues such as diverse thermal flows in industrial sites, complex electricity-heat-cooling energy demands, and unclear industrial-building energy coupling mechanisms, this paper proposes a two-stage planning framework, comprising the following five steps: (1) constructing a waste heat utilization system centered around heat pumps, (2) establishing a mechanism for matching heat sources, equipment, and thermal sinks, (3) establishing an energy conversion model and energy quality quantification system, (4) optimizing waste heat utilization path allocation, and (5) conducting a system energy flow analysis. Case studies demonstrate that the proposed system achieves optimized matching of multiple heat sources and sinks in industrial and building scenarios through thermal integration, meeting diverse energy demands in the park, including electricity, cooling, and multi-grade heat. After coupling with traditional steam pipeline networks, the annual total cost and annual steam power consumption decrease by 33% and 39% respectively, with a waste heat contribution rate of up to 26% and a renewable energy penetration rate of up to 25%, and a waste heat recovery system payback period of 6 years. By establishing an energy quality quantification system and conducting multi-objective optimization considering losses and economic costs, this paper provides Pareto frontier solutions, offering references for engineering proposals. The proposed networked waste heat recovery system is characterized by low energy consumption and high economic efficiency, effectively integrating the energy characteristics of industrial parks and demonstrating engineering applicability.

The pitfall in designing future electrical power systems without considering energy return on investment in planning

Measuring the limitations of transitioning to 100% renewable energy systems partly equates with the capability of quantifying associated net-energy production. To foresee future possibilities, an advanced systemwide energy-return-on-investment (EROI) model was constructed. Driven by the thorough consideration of the existing weaknesses of EROI, the model integrates the concept of energy statistics, life cycle assessment techniques and energy system modelling in order to overcome the underlying primary energy quality issues, boundary condition problems and energy system design related uncertainties that compromises comparability of EROI during the energy transition. This model was applied to the Israeli power system that presents a unique opportunity to examine the vulnerability of depending on limited resources and the connection of EROI to system design. This study demonstrates the dependency of EROI on the energy transition path, system design requirements expressed via interactive linkages of curtailment, variable renewable energy penetration and storage system design, and resource diversity. Achieving a very high variable renewable energy penetration by 2050 with solar photovoltaics carries a risk of falling below an EROI value of 10, such vulnerability can be largely minimised by well-conducted manoeuvring of the system design and resource diversification. Despite the large uncertainty of this study, the need for a multicriteria designing technique is out of the question.

A complete dynamic modeling of two-by-one gas-steam combined cycle unit for coordinated control design

The high penetration of renewable energy can potentially change the role of combined cycle gas turbines (CCGT) from load suppliers to flexible suppliers. However, the slow process in the dynamic modeling of the CCGT complete system restricts the design of its best flexible operation strategy. Therefore, this paper adopts the composite modeling method to establish a complete nonlinear dynamic model of a 2 × 1 CCGT including two gas turbines, two three-pressure heat recovery steam generators (HRSG), one steam turbine, and one heat exchanger. This model can reflect the dynamic characteristics of pressure and flow in the bottom cycle due to the changes in operating mode, which can guide the design of the flexible operation control strategy of the 2 × 1 CCGT. To verify the model’s accuracy, the simulated data were compared with the field data from a 900 MW cycle with the same configuration. The results show that the established model can accurately reflect the changing trend of variables during mode switching. The range errors of essential variables such as gas turbine (GT) power is 3.8 %, GT outlet temperature is 1.4 %, high-pressure drum pressure is 4.5 %, and the range errors of other variables are below 5 %, proving the fidelity of the established control model. The dynamic model established in this paper will be the basis for model-based controller design which can handle multi-objective optimization and constraints in flexible operations. Moreover, this model will be used to guide the design of multi-mode control and energy management strategies.