Minimum Operating Cost Research Articles

Resilient airline scheduling to minimize delay risks

Airlines tend to design their flights schedules with the primary concern of the minimization of operational costs. However, the recently emerging idea of resilient scheduling defined as staying operational in case of unexpected disruptions and adaptability should be of great importance for airlines as well due to the high opportunity costs caused by the flight cancellations and passenger inconvenience caused by delays in the schedule. In this study, we integrate resilient airline schedule design, aircraft routing and fleet assignment problems with uncertain non-cruise times and controllable cruise times. We follow a data-driven method to estimate flight delay probabilities to calculate the airport congestion coefficients required for the probability distributions of non-cruise time random variables. We formulate the problem as a bi-criteria nonlinear mixed integer mathematical model with chance constraints. The nonlinearity caused by the fuel consumption and CO2 emission function associated with the controllable cruise times in our first objective is handled by second order conic inequalities. We minimize the total absolute deviation of the aircraft path variability’s from the average in our second objective to generate balanced schedules in terms of resilience. We compare the recovery performances of our proposed schedules to the minimum cost schedules by a scenario-based posterior analysis.

Distributionally robust dispatching of multi-community integrated energy system considering energy sharing and profit allocation

Community energy consumption accounts for a large proportion of total social energy consumption. Therefore, community integrated energy systems have emerged to satisfy these demands. However, limited resource allocation of the integrated energy system in a single community is possible because of poor anti-interference and insufficient photovoltaic absorption. A distributionally robust optimal dispatching model was proposed for the joint operation of multiple community integrated energy systems, and the energy sharing and joint demand response among communities were considered. Kullback–Leibler (KL) divergence was used to describe the ambiguity set of photovoltaic output probability distribution. When the photovoltaic output prediction error obeyed the extreme probability distribution of ambiguity set, the minimum operation cost of multi-community was solved. Furthermore, to ensure successful alliance of multi-community cooperative alliance, a profit allocation mechanism based on the improved Shapley value was proposed to reallocate the excess return fairly. Finally, a typical multi community integrated energy system was simulated and analysed. The results revealed that energy sharing and joint demand response improve the economy of multi community operation, and the coordination of system robustness and economy of the model was realised. The proposed profit allocation mechanism can effectively ensure multi community cooperation.

Modeling and analysis of cost-effective energy management for integrated microgrids

A microgrid concept is an innovative approach for integrating hybrid and renewable energy sources into the utility grid. The uncertainties because of the intermittent nature of renewable energy resources, the load, and market price are significant challenges. In the traditional heuristic method, data is forecast but not known perfectly. Improving energy storage systems and energy management systems (EMS) development using optimization-based methods is a possible solution to improve the performance of microgrid operations. The EMS is an essential part of the distributed energy resources in the microgrid system, especially when power generation, transmission, distribution, utilization, and variable pricing are involved. This optimization process developed in this paper uses forecasted costs and loading conditions to store or sell the energy from an integrated grid battery system. Two approaches are introduced in this research work: the heuristic method using state flow (chart flow) and the optimization method based on linear programming (LP), which minimizes operation costs (savings of around 19% cost) subject to operational constraints. The LP optimization saves roughly 3.44–5.01% of excess grid energy. Several plausible outcomes of this research study simplify the comprehensive, integrated microgrid simulation for EMS optimization algorithm validation. The suggested integrated microgrid management system might be a testbed for smart grid technology research. • This paper uses linear programming optimization methods to develop a microgrid energy management system (EMS). • A constrained linear program (LP) optimization minimizes the total cost of electricity consumption. • The case studies are presented to show the performance of the proposed optimization and compare it with the heuristic approach. • The method predicts pricing and loading conditions and optimally stores/sells energy from a grid-scale battery system. • The cost of electricity consumption in the optimization method is around 19% less than in the heuristic method.

Efficient Multidimensional Dynamic Programming-Based Energy Management Strategy for Global Composite Operating Cost Minimization for Fuel Cell Trams

Energy management is key to improve the performance of hybrid system. At present, the global optimization-based energy management strategy (EMS) is considered to have the best optimization effect under a known operating condition. However, the traditional global optimization-based EMS is often very time-consuming, which brings great inconvenience to debug and obtain the optimal result. Besides, in the literature, most works still introduce only the fuel consumption into the objective function while failing to consider the durability of fuel cells. In order to make up for these shortcomings, based on the modeling of hybrid system, this article first proposes a global composite operating cost minimization strategy (CMS) both considering fuel consumption and depreciation of purchase cost of fuel cells. Then, this article proposes computationally efficient multidimensional dynamic programming (EMDP) to resolve the optimal control trajectory of the studied issue. Finally, based on the hardware-in-the-loop platform, this article carries out comparative tests of the proposed EMDP algorithm and CMS strategy with traditional ones. Results show that the EMDP algorithm can effectively speed up the computation compared with the traditional dynamic programming. Besides, the proposed CMS strategy would lead to lower operating cost mainly by reducing the degradation cost of fuel cells so that the lifetime of fuel cells could also be extended.

Multi-Objective Optimal Source–Load Interaction Scheduling of Combined Heat and Power Microgrid Considering Stable Supply and Demand

With the development of smart grids, it has become possible to take demand-side resource utilization into account to improve the comprehensive benefits of combined heat and power microgrids (CHP-MGs). In order to improve the benign interaction between the source and the load of the system, the source side decouples the thermoelectric linkage through energy storage devices and improves the system multi-energy supply capacity by introducing various energy flow forms of energy devices. On the demand side, considering the elasticity of electric heating load and the diversity of heating mode, an integrated demand response (IDR) model is established, and a flexible IDR price compensation mechanism is introduced. On this basis, aiming at the optimal stability of supply and demand and the minimum operating cost of the system, a multi-objective optimal operation model of combined heat and power source–load interaction is constructed, taking into account the user satisfaction with energy consumption and the internal equipment load constraints of the system. Finally, an improved multi-objective optimization algorithm is used to solve the model. The analysis of the algorithm shows that the source–load interaction multi-objective optimal scheduling of the cogeneration microgrid considering the stability of supply and demand can effectively improve the stability of supply and demand and the economy of the system.

Operation optimization for park with integrated energy system based on integrated demand response

Industrial parks in China have a large energy consumption with a noticeable peak valley difference between energy consumption and colossal carbon emission. This causes a significant contradiction between energy consumption and energy supply in the country. To solve the above-mentioned problems, an optimization method is proposed for the park integrated energy system based on integrated demand response. First, the energy supply model of the typical day integrated with the energy system in the industrial park during summer was constructed. Second, the price demand response model was established according to the substitution elasticity coefficient. Considering the dynamic characteristics of temperature, the equivalent thermal parameter model and the predicted mean vote (PMV) index model were used to select the adjustable demand response model. Then, aiming at the minimum operation cost and carbon emission, the adaptive chaotic drosophila melanogaster optimization algorithm with niche technology was used to solve this problem, whereby the Pareto optimal solution set was obtained. The VIKOR method was used to select the optimal result comprehensively. Finally, the integrated energy system of an north industrial park was analyzed, and the results were regarded as an example. The results showed that the method proposed by this paper improved the economy considerably and provided environmental protection effectively.

An Implementation of an optimized dual-axis solar tracking algorithm for concentrating solar power plants deployment

A recent assessment of a pilot concentrating solar power plant in Ghana noticed the failure of the tracking system to accurately align the multifaceted heliostats toward the sun and focus the sunbeams onto the target. A further review of solar tracking systems revealed the limitation of concentrated solar power tracking systems to simultaneously improve accuracy and reduce operational cost. In this paper, a computer vision-based solar tracking system, comprising a vision controller, webcam, light sensor, Arduino microcontroller, two stepper motors, a stick, and a transparent plate, is proposed and implemented. The light sensor indicates the presence or absence of sunlight, the webcam captures the image of the stick's shadow on the plate and the vision controller reads this data from the webcam and processes it to extract the solar angles. The angles are sent to the microcontroller which uses them to orient the heliostat appropriately through the stepper motors. The proposed system is location independent and can be installed in each heliostat or a group of heliostats. the preliminary tests show it tracks the sun accurately and exhibits minimal operation cost.

Location Optimization of Emergency Station for Dangerous Goods Accidents Considering Risk

Emergency station is very important for emergency rescue work in hazardous chemical accidents. In order to ensure the efficiency of emergency rescue work, the setting characteristics and objectives of the emergency station should be comprehensively considered. A bi-level programming model of emergency station location for hazardous chemical accidents is established in this paper. The optimization objectives of the model include the minimum risk of emergency station location, the minimum construction and operation cost, the minimum weighted path distance, and the highest coverage level. Based on the NSGA-II algorithm, the model solving technology is designed and applied to the case analysis. The obtained results showed that the efficiency of emergency rescue is continuously improving with the increase in the number of emergency stations.

Renewable Energy Source Based Hybrid Power Plant for Electric Vehicle (EV) Charging Station in Rural Area

An electrical vehicle charging station is a charging power supply for electrical vehicles. This paper proposes design of a model for a PV based electrical vehicle that forecasts total power output under particular conditions of Ankara city. First PV cell parameters are determined and then PV array formed including cells designed in order to calculate cumulative effect. Using actual irradiation and temperature values we try to catch an approximation of output power for the future needs. Electric vehicles offer many advantages ranging from easy access and abundance of electrical energy sources. The objective of this paper is to obtain the best configuration of the hybrid power systems for charging station in a rural area such as Labuan bajo, Indonesia. Thus, the best configuration obtained is then installing with three types of energy storage namely Lead Acid and UNS Lithium battery such as Lithium Ion and Lithium Ferro Phosphate (LFP) to determine the minimum cost of operation and energy cost in a year. The results showed by implementing hybrid systems from PV and DER is the best configuration for off grid charging station. The most optimal battery in off grid system achieved by installing UNS LFP batteries. As a conclusion, by utilizing hybrid power generation technology, the potential for renewable energy in rural areas can be the main key in realizing the availability of charging stations in rural areas with affordable price for supporting electric vehicles infrastructure.

Integrated Optimization of Transit Networks with Schedule- and Frequency-Based Services Subject to the Bounded Stochastic User Equilibrium

In many European metropolitan areas, the urban transit system is a mixture of schedule- and frequency-based services. This study proposes an integrated transit frequency and schedule design problem (ITFSDP), where frequencies and schedules are simultaneously determined, and develops a biobjective model for the ITFSDP to minimize operation costs and total passenger-perceived generalized travel cost. Meanwhile, the passengers’ route choice behavior is described by the bounded stochastic user equilibrium (BSUE). The in-vehicle congestion effect is represented using a set of constraints that differ in terms of the sitting and standing costs as sitting and standing passengers perceive crowding differently. This set of constraints captures the realistic behavioral feature that having occupied a seat, users remain seated at subsequent stops in the same vehicle. The problem is formulated as a mixed integer nonlinear programming problem, which is subsequently linearized to a mixed integer linear programming problem and solved using a branch and bound algorithm. A column generation and reduction phase is embedded in the solution algorithm to obtain the bounded choice set according to the BSUE constraints. Experiments are conducted to illustrate the model’s properties and the performance of the solution method. In particular, we demonstrate a Braess-like paradoxical phenomenon in the context of transit scheduling and highlight that well-synchronized transit services can deteriorate the network performance in terms of the total passengers’ generalized travel cost when considering passenger congestion costs because of crowding.

Annual Operating Costs Minimization in Electrical Distribution Networks via the Optimal Selection and Location of Fixed-Step Capacitor Banks Using a Hybrid Mathematical Formulation

The minimization of annual operating costs in radial distribution networks with the optimal selection and siting of fixed-step capacitor banks is addressed in this research by means of a two-stage optimization approach. The first stage proposes an approximated mixed-integer quadratic model to select the nodes where the capacitor banks must be installed. In the second stage, a recursive power flow method is employed to make an exhaustive evaluation of the solution space. The main contribution of this research is the use of the expected load curve to estimate the equivalent annual grid operating costs. Numerical simulations in the IEEE 33- and IEEE 69-bus systems demonstrate the effectiveness of the proposed methodology in comparison with the solution of the exact optimization model in the General Algebraic Modeling System software. Reductions of 33.04% and 34.29% with respect to the benchmark case are obtained with the proposed two-stage approach, with minimum investments in capacitor banks. All numerical implementations are performed in the MATLAB software using the convex tool known as CVX and the Gurobi solver. The main advantage of the proposed hybrid optimization method lies in the possibility of dealing with radial and meshed distribution system topologies without any modification on the MIQC model and the recursive power flow approach.

Pressure Drop Optimization of the Main Steam and Reheat Steam System of a 1000 MW Secondary Reheat Unit

The pressure drop of a main steam and reheat steam system should be optimized during the design and operation of a thermal power plant to minimize operation costs. In this study, the pressure drop of the main steam pipe and reheat steam pipe of a 1000 MW secondary reheat unit are optimized by modulating the operation parameters and the cost of operation is explored. Optimal pipe specifications were achieved by selecting a bend pipe and optimizing the pipe specifications. The pressure loss of the main steam pipeline was optimized to 2.61% compared with the conventional pressure drop (5%), the heat consumption of steam turbine was reduced by about 0.63 kJ/(kW·h), the standard coal consumption was minimized by about 0.024 g/(kW·h), and the total income in 20 years is approximated to be CNY 217,700. The primary reheat system was optimized to 4.88%, the steam turbine heat consumption was reduced by about 7.13 kJ/(kW·h), the standard coal consumption decreased by about 0.276 g/(kW·h), and the total income in 20 years is projected to be CNY 20.872 million after the optimization of the pressure drop. The secondary reheat system was optimized to 8.13%, the steam turbine heat consumption was reduced by about 7.86 kJ/(kW·h), the standard coal consumption decreased by about 0.304 g/(kW·h), and the total income in 20 years is projected to be CNY 22.7232 million after the optimization of the pressure drop. The research results of the present study provide a guide for the design and operation of secondary reheat units to achieve an effective operation and minimize costs.

A Novel Optimization Model of Integrated Energy System considering Thermal Inertia and Gas Inertia

Aiming at the energy multivariate heterogeneity of thermal system and natural gas system, a novel optimization model of integrated energy system considering thermal inertia and gas inertia is proposed. First, the dynamic characteristics of the thermal system in the integrated energy system are studied, and the inertia model of the heat network pipeline and thermal building is established. Second, the characteristics of natural gas pipeline network storage are studied, and the natural gas storage and pressure energy generation models are established. Then, the optimization objective of the minimum integrated operating cost of the integrated energy system is established, and the YALMIP solver is used to solve it. Finally, a numerical example is introduced to analyze the operating cost of the integrated energy system in different scenarios, and it is verified that the integrated energy system optimization model considering the inertia of the thermal and gas proposed in this paper can effectively improve the system regulation capability and reduce the system operating cost.

Distributionally Robust Optimal Dispatching of CHP Microgrid considering Concentrating Solar Power and Uncertainty

In order to solve the problem of diversified low-carbon energy supply with renewable energy as the main body, concentrating solar power (CSP) stations are introduced to act as cogeneration units. Taking full advantage of the flexible coupling and multienergy complementarity of electric, heat, and gas, an economic dispatch method for combined heat and power microgrid systems (CHP microgrid) with interconnected electric, heat, and gas is proposed. First, build the CSP-CHP microgrid structure and model the main equipment. Then, aiming at the minimum operating cost of the system, a regular scheduling model of the CSP-CHP microgrid system is established. On this basis, in order to deal with the uncertainty of renewable energy output, a distributionally robust optimization (DRO) model is introduced. In the DRO model, the Kullback–Leibler (KL) divergence is used to construct an ambiguity set about the predicted error of renewable energy output, and finally, the CSP-CHP microgrid DRO economic dispatch model is established. Finally, the system is simulated and analyzed in a typical CSP-CHP microgrid system, and the feasibility and effectiveness of the proposed method are verified by analysis. In addition, the necessity of introducing CSP and the advantages of the DRO model is further explained by comparison.

A Probabilistic Model for Minimization of Solar Energy Operation Costs as Well as CO2 Emissions in a Multi-Carrier Microgrid (MCMG)

This paper proposes a probabilistic model with the aim to reduce the solar energy operation cost and CO2 emissions of a multi-carrier microgrid. The MCMG in this study includes various elements such as combined heat and power (CHP), electrical heat pump (EHP), absorption chiller, solar panels, and thermal and electrical storages. A MILP model is proposed to manage the commitment of energy producers, energy storage equipment, the amount of selling/buying of energy with the upstream network, and the energy consumption of the responsible electrical loads for the day-ahead optimal operation of this microgrid. The proposed operation model is formulated as a multi-objective optimization model based on two environmental and economic objectives, using a weighted sum technique and a fuzzy satisfying approach. In this paper, the 2 m + 1-point estimate strategy has been used to model the uncertainties caused by the output power of solar panels and the upstream power supply price. In order to evaluate the performance of the proposed model, and also for minimizing cost and CO2 emissions, the simulation was conducted on two typical cold and hot days. Numerical results show the proposed model’s performance and the effect of electrifying the heating and cooling of the microgrid through the EHP unit on greenhouse gas emissions in the scenarios considered.

Integrated inventory and transportation management with stochastic demands: A scenario-based economic model predictive control approach

The integration of inventory and transportation operations is a challenging task in multi-echelon supply chain management. Uncertainties in customer demands can have serious consequences, such as inventory fluctuations and transportation changes. Policies of integrated inventory and transportation management aim to find a balance between supply and demand and can minimize the total operating cost for the entire supply chain. In this study, we propose a scenario-based economic model predictive control (EMPC) framework for integrated inventory and transportation management of multi-echelon supply chains with stochastic demands. The proposed scenario-based EMPC framework can provide real-time optimal operations for automated inventory and transportation management with a minimum operating cost and a guaranteed risk probability. As the number of scenarios is linked with the risk probability, we develop a design condition to choose the number of scenarios and ensure that optimal operations from the proposed framework can guarantee the desired risk probability. Finally, we present a case study of a three-echelon supply chain benchmark to demonstrate the effectiveness of the proposed EMPC framework and provide several comparison results to show its performance.

Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility

Aggregated electric vehicles (EVs) integrated to the grid and intermittent wind and solar energy increased the complexity of the economic dispatch of the power grid. Aggregated EVs have a great potential to reduce system operating costs because of their dual attributes of load and energy storage. In this paper, plugged-in EV is refined into three categories: rated power charging, adjustable charging, and flexible charging–discharging, and then control models are established separately; the concept of temporal flexibility for EV clusters is proposed for the adjustable charging and flexible charging–discharging of EV sets; then, the schedule boundary of EV clusters is determined under the flexibility constraints. The interval is used to describe the intermittent nature of renewable energy, and the minimum operating cost of the system is taken as the goal to construct a distributed energy robust optimization model. By decoupling the model, a two-stage efficient solution is achieved. An example analysis verifies the effectiveness and superiority of the proposed strategy. The proposed strategy can minimize the total cost while meeting the demand difference of EV users.

Optimization of the operation plan taking into account the flexible resource scheduling of the integrated energy system

Under the high proportion of renewable energy access in the integrated energy system, the insufficient total allocation of flexible resources and the unreasonable spatial allocation of resources are important causes of wind abandonment and load shedding. The fundamental solution to this problem is to increase the flexibility of the system to meet demand, so it is important to study multiple types of flexible resources, the state of supply and demand for system flexibility and the optimization of flexibility resource scheduling. This paper studies the flexibility of the integrated energy system. First, it analyzes the basic characteristics of the flexible resources of the system and gives the basic structure of the integrated energy system;Then, the typical flexible resources are modeled and analyzed. Based on the time-of-use price, the minimum daily operation cost of the system is optimized as the objective function, and the particle swarm optimization algorithm is introduced to solve it, so as to explore the flexible adjustment of the system with the participation of flexible resources;Finally, the model established in this paper is used to verify the rationality of the scheduling scheme, and the relationship between the access effect of flexible resources and the location of access nodes is explored. The research on the modeling of integrated energy system and system flexibility in this paper not only provides a tool to reflect the fluctuation of system supply and demand state, but also provides an important basis for system reasonable scheduling, ensuring energy supply quality and improving operation flexibility.

Statistical modeling and optimal energy distribution of cogeneration units by genetic algorithms

Our main objective is to evaluate the performance of a new method to optimize the energy management of a production system composed of six cogeneration units using artificial intelligence. The optimization criterion is economic and environmental in order to minimize the total fuel cost, as well as the reduction of polluting gas emissions such as COx, NOx and SOx. First, a statistical model has been developed to determine the power that the cogeneration units can provide. Then, an economic model of operation was developed: fuel consumption and pollutant gas emissions as a function of the power produced. Finally, we studied the energy optimization of the system using genetic algorithms (GA), and contribute to the research on improving the efficiency of the studied power system. The GA has a better optimization performance, it can easily choose satisfactory solutions according to the optimization objectives, and compensate for these defects using its own characteristics. These characteristics make GA have outstanding advantages in iterative optimization. The robustness of the proposed algorithm is validated by testing six cogeneration units, and the obtained simulation results of the proposed system prove the value and effectiveness of GA for efficiency improvement as well as operating cost minimization.

A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources

This paper presents a new framework for the scheduling of microgrids and distribution feeder reconfiguration (DFR), taking into consideration the uncertainties due to the load demand, market price, and renewable power generation. The model is implemented on the modified IEEE 118-bus test system, including microgrids and smart homes. The problem has been formulated as a two-stage model, which at the first stage, the day-ahead self-scheduling of each microgrid is carried out as a two-objective optimization problem. The two objectives include the minimization of the total operating cost and maximization of the consumer's comfort index. Then, the solution, obtained from the first stage is delivered to the distribution system operator (DSO). Then, at the second stage, the DSO determines the optimal configuration of the system with the aim of minimizing operating costs of the main grid and the penalty of deviating from microgrid scheduling. Note that the penalty is due to the difference in power exchange requested by the microgrids from the power exchange finalized by the DSO. The presented two-stage optimization problem is modeled in a mixed-integer linear programing (MILP) framework with four case studies, and solved in GAMS by using the GURUBI solver. The simulation results show that in the cases the DSO is able to reconfigure the system, the deviation from the optimal scheduling of microgrids would be considerably lower than the cases with fixed system configuration. • Proposing a two-stage optimization framework for scheduling of microgrids and DFR. • Considering smart homes and traditional loads in the model. • Two-objective modeling of microgrids' scheduling problem by the fuzzy method. • Solving the DFR problem with regard to the optimal scheduling of microgrids. • Investigating the impact of different objective functions on smart homes' scheduling.