Dispatch Model Research Articles

Slot co-chartering and capacity deployment optimization of liner alliances in containerized maritime logistics industry

With the growing traffic of containerized shipping worldwide, container liners have seen increasing cooperation. Slot co-chartering has drawn wide attention as a way of cooperation within liner alliances. However, liners face the issue of finding proper measures to optimize alliance operations. In response to this, this paper comprehensively considers shipping capacity dispatch for containerized shipping companies within liner alliances. First, we explained the general rules of liner shipping dispatch and analyzed the interfering factors. Second, we created a liner shipping capacity dispatch model without consideration of slot co-chartering costs and a liner shipping capacity dispatch model with consideration of slot co-chartering costs and proposed a new column generation algorithm to solve both problems. Third, we tested our algorithm in a real case of a large Chinese containerized shipping company, which belongs to Ocean Alliance, and an optimal ship dispatch strategy on relevant routes and a decision on optimal slot chartering can be reached. Computational results indicate that the proposed column generation algorithm exhibits effective and efficient performance for large-scale slot co-chartering in liner shipping.

Bilevel Optimal Economic Dispatch of CNG Main Station Considering Demand Response

Compressed natural gas (CNG) main stations are critical components of the urban energy infrastructure for CNG distribution. Due to its high electrification and significant power consumption, researching the economic operation of the CNG main station in demand response (DR)-based electricity pricing environments is crucial. In this paper, the dehydration process is considered in the CNG main station energy consumption model to enhance its participation in DR. A bilevel economic dispatch model for the CNG main station is proposed, considering critical peak pricing. The upper-level and lower-level models represent the energy cost minimization problems of the pre-system and rear-system, respectively, with safety operation constraints. The bilevel programming model is solved using a genetic algorithm combined with a bilevel programming method, which has better efficiency and convergence. The proposed optimization scheme has better control performance and stability, reduces the daily electricity cost by approximately 21.04%, and decreases the compressor switching frequency by 50.00% without changing the CNG filling demand, thus significantly extending the compressor’s service life. Moreover, the average comprehensive power cost of processing one unit of CNG reduces 20.62%.

Electric vehicle navigation and cluster dispatch for reliable low-carbon traffic–Power systems

Large-scale integration of electric vehicles (EVs) into the city system for charging will affect the operation of both traffic and distribution networks. An electric vehicle navigation and cluster dispatch model is proposed for improving the overall charging efficiency of EVs on the transportation network and increasing the voltage level of the distribution network. First, a simplified model of vehicles and traffic road network is established, and a cell transmission model (CTM) is used to simulate the real traffic network. The traffic system takes into account charging EVs, discharging EVs, and other vehicles, and traffic congestion is considered. Then, a coupled model of the traffic–power system is built for the orderly charging of electric vehicles upon arrival at a charging station. The model considers the coupling of the two systems on a time scale, and the charging/discharging power at each charging station node is controlled. The validity of the model is verified in a coupled system of 357 cell traffic network and modified IEEE33 nodes. The results show that the proposed model can ensure good guarantee of the distribution network voltage reliability and reveal the scheduling process of the traffic network. The proposed model also provides a reference for planning of charging stations in the distribution network.

Assessing how non-carbon co-priorities affect zero-carbon electricity system development in California under current policies

While many electricity resource mixes can facilitate a zero-carbon electricity system, different pathways can vary significantly in their contribution to environmental impacts. Many current assessments focus on tradeoffs associated with monetary cost, neglecting these wider impacts. Here, electric grid dispatch modeling and electricity mix optimization is combined with data on resource consumption and electricity technology costs to compare five different approaches for developing a 100% zero-carbon electricity system in California: minimum critical metals use, minimum solid construction materials mass, minimum land use, minimum freshwater consumption, and minimum monetary cost under present-day policy goals and constraints. The modeled scenarios show that prioritizing minimum solid construction materials mass in developing such systems also achieve near-minimal monetary cost and land use and did not exhibit the worst performance on either freshwater consumption or critical metals use. In contrast, the strategy that prioritized minimum freshwater consumption exhibited the largest land use and materials use of the five strategies. The minimum monetary cost strategy exhibited near-minimal freshwater consumption, but large land use and the highest demand for critical metals. The modeled monetary unit cost of electricity was lower than the 2030 reference for all zero-carbon electricity system scenarios. The results highlight tradeoffs between contributions to different types of environmental impact in developing a zero-carbon electricity system. Notably, prioritizing certain metrics can result in electricity systems that balance these tradeoffs better than others given the existing suite of zero-carbon options. More broadly, the results show that the planning of zero-carbon electricity systems should more explicitly incorporate non-carbon environmental externalities as co-priorities in their development.

Day-ahead dispatch of novel battery charging and swapping station based on distributionally robust optimization

Battery swapping station (BSS) is a promising way to support the proliferation of electric vehicles (EVs). This paper upgrades BSS to a novel battery charging and swapping station (NBCSS) with wind power, photovoltaic power, energy storage and gas turbine integrated, which is equivalent to a microgrid with flexibility further enhanced. An integrated model of batteries based on the state of charge (SOC) interval is put forward to release the complexity of separate modeling of each battery, where the charging and discharging priorities are embedded. Then, a distributionally robust optimization (DRO) model for the day-ahead dispatch of NBCSS is presented considering the uncertainties of wind power, photovoltaic power and load. This model minimizes the worst-case expected total cost over a family of distributions characterized by an ambiguity set. By employing the affine decision rules, the primitive two-stage DRO model can be eventually reformulated as a tractable mixed-integer linear program. Finally, case studies are conducted to demonstrate the effectiveness of the proposed method. The results show that the charging and discharging freedom of batteries enhances the operational flexibility of NBCSS and reduces the 22.9 % of the total cost. And the proposed method has the superiority of decision-making over the deterministic and adaptive robust optimization ones.

Stochastic Security-Constrained Economic Dispatch of Load-Following and Contingency Reserves Ancillary Service Using a Grid-Connected Microgrid during Uncertainty

In the context of the growing penetration of renewable power sources in power systems causing probabilistic contingency conditions, a suitable economic dispatch model is decisively needed. There is a lack of research in the field of probabilistic mathematical formulation considering the uncertainties due to the stochastic nature of renewables and contingency occurrence, as it is a very complex problem to be solved. The most appropriate model is the stochastic security-constrained economic dispatch (SSCED) model for optimized economic dispatch decisions during uncertainty. However, because of its complexity, it is rarely employed. This paper attempts to solve the complex SSCED problem in the presence of the uncertainty of resources and probabilistic contingency conditions, which is a novel effort in this regard. The SSCED is carried out over multiple periods to provide the load-following or contingency reserves. In the proposed SSCED, the uncertainty problem is addressed by modeling the stochastic wind energy power source by using “probability transition scenarios”. The uncertainty caused by probabilistic contingency conditions in the dispatch schedule is approximated using a “state-specific transition matrix”. The frequency control reserves in contingency conditions are co-optimized with energy, and stochastic security-constrained economic dispatch is achieved. The efforts are put forward to suggest a new market model in the presence of the uncertainty of renewable energy availability. Case studies are examined to show the potential technical and financial advantages of the proposed SSCED through co-optimization. Grid-connected microgrid owners offer frequency control ancillary services by providing load-following ramping reserves in the normal state and contingency reserves in the state of contingency. The probabilistic contingencies considered are generator failure and an underloading condition. A modified “IEEE 30 bus system” is considered a grid-connected microgrid for testing the proposed SSCED. The results show that the greater the flexibility of the resources, the greater the technical and economic benefits. The increase in ramping flexibility of a wind source results in almost an 8.1% reduction in operational costs compared to the base case. The contingency condition analysis shows that the presence of ramping reserves in the system enhances the power system performance, avoiding the cascading effects that ultimately cause a power system failure.

Low-carbon economic dispatch of integrated energy system considering carbon trading mechanism and LAES-ORC-CHP system

Under the theme of low carbon, in order to improve the economy of integrated energy system (IES), optimize the operational flexibility of equipment and further reduce the carbon emission level of IES, a low-carbon economy operation strategy of the IES is proposed. First, the IES is considered to participate in the carbon trading market, and the carbon trading mechanism is introduced to control the carbon emissions of the system. Second, on the basis of the working characteristics of liquid air energy storage (LAES), organic Rankine cycle (ORC), and combined heat and power (CHP), the LAES-ORC-CHP system is established in the IES to achieve flexible supply of heat and power. Then, aiming at minimizing the sum of system operation costs and carbon transaction costs, the low-carbon economy optimal dispatch model of IES is proposed. Finally, the effectiveness of the proposed model is verified by setting multiple operation scenarios. Simulation results show that the proposed model can fully consider and balance the economy and low carbon level of the system and provide a reference for the low-carbon economic dispatch operation of the IES.

Digital twins for secure thermal energy storage in building

The purpose of this work is to explore the role of the safe and optimal scheduling of thermal energy storage systems in intelligent buildings in promoting sustainable economic development under Digital Twins (DTs) technology. Phase Change Material (PCM) has high energy density, constant temperature storage, small footprint, and long service life. Here, PCM is first placed in the indoor building structure, and the DTs technology is introduced. In the development of intelligent buildings, the data generated by the energy storage system of intelligent buildings in the real space can be mapped to the virtual space in real time for simultaneous analysis. In addition, the PCM wall structure and thermal network DTs model are designed for the intelligent building. In addition, the PCW structure is used to build a thermal energy storage and dispatch model of the smart thermoelectric building based on DTs. Finally, the model is evaluated and analyzed experimentally. The analysis of system optimization power under different schemes indicates that the scheduling operation strategy of thermal energy storage of building walls can avoid overcharging or over-discharging batteries in the microgrid and reduce battery power consumption. Besides, the building wall energy storage capacity is always in the range of 0.2 ∼ 0.8 on the all-weather scale. Moreover, the model constructed here achieves significantly lower economic costs, environmental costs, and energy costs and a better energy-saving effect than the existing model. The model built here can serve as experimental reference for further digital energy storage in intelligent buildings and comprehensive energy utilization because of its superior safety performance and lower consumption.

A Dynamic Patients Dispatch and Treatment Model for Resilience Evaluation of Interdependent Transportation-Healthcare System

ABSTRACT Emergency authorities are concerned with how to effectively deliver patients to different hospitals for treatment while not overwhelming any single hospital during emergencies. This study proposes an optimization model for the real-time dispatching and treating schedule to evaluate the seismic resilience of the interdependent transportation-healthcare system. The functionality of the road network, the service of hospitals, and emergency management are comprehensively considered in the model. A series of case studies were used to test the applicability of the method and quantify the resilience under three seismic intensities.

Nuclear Fusion impact on the requirements of power infrastructure assets in a decarbonized electricity system

Electrification of final energy use and electricity generation by low-carbon technologies are key points of the path toward carbon-neutrality. Carbon free electricity can be generated by both nuclear and renewable energy sources. Nevertheless, although all of them can be economically viable in terms of ‘Levelized Cost Of Energy‘, their exploitation involve variable renewables, so as to require power system upgrades with adequate energy storage systems, dispatchable generation capacity and transmission/distribution grid enhancements (power infrastructure assets) that may lead to relevant additional system costs. To this respect, each power system is almost unique, due to its peculiarities as far as renewable potentials is concerned. In order to find the least cost feasible and reliable generation mix, detailed hourly simulations are necessary. In this paper, long term power generation scenarios will be simulated with the COMESE code, a dispatch model able to perform detailed regional power systems analysis. Carbon-free Italian power system long term scenarios are simulated, with high share of photovoltaic and to less extent wind electricity together with a possible contribution of nuclear fusion power plants. The operation of the transmission grid is simulated, through a transport model, in order to assess the necessary grid enhancement and to estimate the related costs. In this context the impact of fusion will be assessed in terms of mitigation of the overall system cost of electricity.

The energy storage and optimal dispatch supply chain for new energy grids using edge computing and the internet of things

AbstractThe intermittent and uncertainty of new energy in the grid connection process affects the overall quality of the grid. To resolve the scattered geographical locations, small individual capacities and poor controllability of distributed energy storage (DES) devices, edge computing is applied in conjunction with aggregate service providers to build an edge computing‐based aggregate model of DES. Regarding its characteristics, the schedulable potential of the energy storage device is analysed through modelling. A multi‐objective scheduling model with multiple parties participating in the subject's behaviour is established using the Internet of Things (IoT) optimization strategy. The simulation example of concrete data further verifies the validity of the model. Results demonstrate that the edge computing‐based aggregate model of DES effectively reduces the calculation load's peak‐valley load and reduces the wind power abandonment. In the meantime, the multiple starts and stops of the thermal power unit reduce the operation cost of the unit. The multi‐objective dispatch model can reduce the opportunity cost and payment of DES effectively. This model achieves load peak reduction and valley filling and reduces the peak dispatch cost of the power grid. The research results can provide some ideas for storing and utilizing the new energy.

Impact Assessment and Defense for Smart Grids With FDIA Against AMI

Cyber-physical security has been becoming an important issue for many critical infrastructures in human society, including electrical power grids. This paper proposes an impact assessment framework for smart grids suffering false data injection attack against smart meters. Firstly, the propagation model of malicious false data codes in smart meter communication networks is established. Based on this, a security-constrained economic dispatch model is formulated to assess how false data codes would mislead the grid operator's actions. A defense strategy is proposed, which enhances the grid's vulnerability through effectively deploying defense resources in smart meter networks. Extensive numerical simulations are conducted to evaluate the impact of different attack scenarios as well as the effectiveness of the defense strategy.

Hierarchical AGC Dispatch With Detailed Modeling of Energy Storage System Behaviors

The key challenge for automatic generation control (AGC) dispatch lies in the contradiction between the detailed modeling required for optimal dispatch and the tight calculation time. The current method includes (1) the heuristics method that allocates real-time commands based on certain rules (fast but nonoptimal) and (2) the proactive dispatch method with a detailed AGC dispatch model (relatively optimal but the forecast error exists). The development of renewables and the prevalence of energy storage systems (ESSs) with costly degradation calls for combining the advantages of heuristics and proactive methods in AGC dispatch. With this in mind, a hierarchical AGC dispatch method is proposed. A proactive AGC dispatch model embedding state of charge (SOC) recovery is established to make the best use of regulating resources while avoiding the degradation of ESSs. A uniform SOC model considering comprehensive ramping circumstances of ESSs is proposed with low model complexity. A theoretical analysis demonstrates the desired modeling accuracy. To compensate for the forecast error, real-time AGC dispatch is considered. The dead band and forced SOC recovery of ESSs are used to alleviate their degradation. Time-domain simulations based on practical data demonstrate that the proposed method can improve the system frequency performance while obviously reducing the degradation of ESSs.

Multiobjective Optimal Dispatch of Mobile Energy Storage Vehicles in Active Distribution Networks

In active distribution networks (ADNs), mobile energy storage vehicles (MESVs) can not only reduce power losses, shave peak loads, and accommodate renewable energy but also connect to any mobile energy storage station bus for operation, making them more flexible than energy storage stations. In this article, a multiobjective optimal MESV dispatch model is established to minimize the power loss, renewable energy source curtailment, and total operating cost of ADNs. Additionally, a method to directly obtain the compromise optimal solution (COS) of the Pareto optimal solutions (POSs) of a three-objective optimization problem is proposed. Based on the normalized normal constraint (NNC) method for solving the POSs of a three-objective optimization problem, a bilevel optimization model is established to directly obtain the COS. The innerlayer model uses the NNC method to calculate the POS corresponding to a point on the Utopia plane, and the outerlayer model uses the improved Nelder–Mead (INM) method to directly search the POS with the minimum distance to the Utopia point as the COS. Compared with the traditional method, which first solves the POSs of the three-objective optimization problem and then selects the COS, the proposed INM method has higher computational efficiency and obtains a better COS. Finally, case studies on the modified IEEE-33 bus distribution network and an actual 180 bus distribution network demonstrate the effectiveness of the proposed method.

Research on the Economic Dispatch of Power Systems with a High Proportion of Renewable Energy based on Improved PSO

The penetration rate of renewable energy sources represented by wind power and photovoltaics in the power grid is increasing, adding complexity to unit dispatch. In this paper, a multi-objective optimization model is established for power systems containing wind power and photovoltaic economic dispatch. Firstly, the power generation cost of traditional units, the emission cost of traditional units, the power generation costs of wind turbines and photovoltaics, and the output characteristics of wind power and photovoltaics are analyzed. Secondly, a multi-objective economic dispatch model with the lowest power generation cost, the smallest emission cost, and the smallest power generation cost of new energy units are established, and the power flow balance constraint, supply and demand balance constraint, unit output constraint, and node voltage constraint are considered. Then the improved particle swarm algorithm is proposed to solve the multi-objective function to obtain better convergence and optimal solution. Finally, the simulation is carried out in the IEEE-30 node system and the HRP-38 system, which compares and illustrates the similarities and differences of the solution results of various algorithms under multi-objective and verifies the effectiveness of the algorithm.

Intraday markets, wind integration and uplift payments in a regional U.S. power system

U.S. electricity markets adopt a two-settlement structure with day-ahead and real-time markets. Wind power integration poses a challenge for this market structure because day-ahead wind production forecasts are uncertain and significant rescheduling costs may occur in real time, increasing the need for out-of-market uplift payments. We investigate whether a multi-settlement structure with four intraday stages taking place 18 to 3 h before electricity generation reduces uplift payments and system costs, relative to a two-settlement structure. We develop unit commitment and dispatch models that account for intertemporal constraints and solve them on a 36-node test system using historical wind forecasts provided by a Regional Transmission Organization, as well as synthetic wind forecasts reproducing the observed correlation in the historical data. Combining historical and simulated forecasts allows us to account for wind uncertainty and draw more robust conclusions about the relative merits of each market structure. Finally, we compare the performance of market designs under varying levels of wind penetration. Under any level of wind penetration, the proposed multi-settlement market design is more likely to yield higher uplift than the two-settlement market design. Wind forecast accuracy and structural differences between the U.S. and European electricity markets are key drivers of our results.

Comprehensive study on cascade hydropower stations in the lower reaches of Yalong river for power generation and ecology

According to the construction of the cascade hydropower stations in the lower reaches of the Yalong River and the ecological environment condition of the river, a medium and long-term optimal dispatching model with the goal of maximizing the power generation benefits of the cascade hydropower stations is established, and the dynamic programming method is used to optimize the solution, so as to obtain the ten-day average maximum power generation of cascade hydropower stations. According to the latest relevant research at home and abroad, it is found that during the power generation process of the hydropower station, the discharge will produce certain greenhouse gas (GHG) emissions during the delivery process. In this paper, the greenhouse gas emissions existing in the power generation process of cascade reservoirs are considered, and the typical values are selected to calculate the average greenhouse gas (mainly CO2) emissions from 2001 to 2018, twelve plants with strong carbon sequestration and oxygen release capacity and suitable for growing in the basin are selected, and the mitigation and improvement measures of greenhouse gas emissions around the reservoirs in the basin are given. A new research idea is provided for the Yalong River cascade reservoirs to focus on the power generation benefits while reducing the greenhouse gas emission situation to a certain extent.

Optimal dispatch of integrated energy systems considering integrated demand response and stepped carbon trading

The integrated energy system is an effective way to achieve carbon neutrality. To further exploit the carbon reduction potentials of IESs, an optimal dispatch strategy that considers integrated demand response and stepped carbon trading is proposed. First, an integrated demand response (IDR) pricing approach is proposed based on the characteristics of different load types. Classify multi-energy loads into curtailable and substitutable loads, and incentivize both loads through a price elasticity matrix and low-price energy in the same period. Then, to better incentivize IESs to reduce carbon emissions, a stepped pricing mechanism was introduced in the carbon price. Finally, an optimal dispatch model is developed with an objective function that minimizes the sum of energy purchase cost, carbon trading cost, and operation and maintenance (O&M) cost. Considering the high-dimensional and non-linear characteristics of the model, an improved differential evolution (DE) algorithm is introduced in this paper. In addition, this paper also analyzes the effects of the stepped carbon trading parameters on the optimal dispatching results of the system in terms of carbon trading base price, carbon emission interval length, and carbon price growth rate. Compared to the case of adopting a single IDR model or a single stepped carbon trading, carbon emissions from the IESs decreased by 6.28% and 3.24%, respectively, while total operating costs decreased by 1.24% and 0.92%, The results show that the model proposed in this paper has good environmental and economic benefits, and the reasonable setting of stepped carbon trading parameters can effectively promote the low-carbon development of IESs.

Optimal dispatch of integrated energy station considering carbon capture and hydrogen demand

To respond to the escalating contradiction between improving economy and reducing carbon emissions, a low-carbon optimal dispatch model of integrated energy station (IES) considering carbon capture and hydrogen demand is proposed. First, a wind-photovoltaic-hydrogen power (WPHP) integration model is built, giving an effective way of renewable energy accommodating and reliable hydrogen supplying of IES. The WPHP significantly improves system economy and decreases the renewable energy curtailment, especially with a high proportion integrated; Then, a coupling model of carbon capture system (CCS) and power-to-gas (P2G) is built. The model resolves the problem of carbon source of P2G and reduces the carbon net emissions, changing the carbon trading cost from expenditure to profit; Next, an integrated demand response (IDR) model is built covering electricity, gas, cooling, hydrogen load as well as the heating load with different heat grade requirements. The IDR further improves multi-energy users' satisfaction, the economy and stability of IES by decreasing the peak-valley difference and increasing load ratio of multi-energy loads. Finally, an optimal dispatch model of IES is built with the objective of minimizing the cost of economic, carbon emission trading and users’ dissatisfaction, and the effectiveness of the model is verified by a numerical example.

A robust optimal dispatching strategy of distribution networks considering fast charging stations integrated with photovoltaic and energy storage

With the increasing number of electric vehicles, a large number of charging loads connected to the power system will have an impact on the economic and safe operation of the power system. In this paper a day-ahead optimal dispatching method for distribution network (DN) with fast charging station (FCS) integrated with photovoltaic (PV) and energy storage (ES) is proposed to deal with the negative impact of FCS on DN. By adjusting the load distribution of DN through the optimization decision of ES and soft open points (SOP), the operation level of DN is improved. Firstly, based on the historical vehicle travel data, Monte Carlo simulation method (MCSM) is applied to realize the simulation of fast charging load. Secondly, the uncertainties of PV power is addressed via a robust optimization model of the economic operation level of DN. Based on the second order cone relaxation and duality theory, a two-stage optimal dispatching model of DN is proposed. The optimization model is divided into main problem (MP) and sub problem (SP). For MP, the access position of FCS is adjusted based on SOP. And the charging and discharging power of ES is adjusted. The load distribution is optimized. For SP, based on the uncertainty of PV, the worst scenario of DN is calculated. The robustness of the proposed strategy is guaranteed. Finally, the proposed is verified based on the IEEE 33 bus system and a road network with 34 nodes. The simulation results show that the proposed method can effectively relieve line congestion of DN. The operating range of the voltage is better optimized. And the operation cost of DN is reduced significantly.