Optimal Strategies for Hybrid Battery‐Storage Systems Design

The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because of its high operational flexibility. In the MES, the hybrid energy storage system (HESS) composed of the battery and thermal storage tank plays an important role in enhancing reliability, economics, and operational flexibility. Hence, determining the optimal size of HESS in the MES is a critical problem but has not received enough attention. In light of this problem, this paper focuses on the optimal HESS planning problem in the community MES (CMES) under diverse uncertainties. Firstly, a two-stage stochastic planning model is proposed for the CMES to coordinate the optimal long-term HESS allocation and the short-term system operation. The thermal inertia in the heating network, space heating demand, and domestic hot water demand is utilized to reduce both the planning and operational cost. Secondly, a deterministic equivalence is proposed for the two-stage planning model to convert it into a mixed-integer linear programming model, which is then solved by off-the-shelf solvers. Finally, simulation results verify the effectiveness of the proposed method. The results reveal that the HESS can enhance the operational flexibility of the CMES but only needs a very few investment costs and prove that the thermal inertia in the CMES can reduce the investment cost of HESS, the fuel, and the operational maintenance cost.

Cellular manufacturing systems (CMS) are production systems that typically comprise a number of manufacturing cells served by a centralized material handling system. Designing such systems includes three major decisions; cell formation (CF), group layout (GL), and group scheduling (GS). Traditionally, these three decisions have been dealt with separately, which has usually lead to less than optimal system performance. In this paper, a new mixed integer linear programming (MILP) model is proposed for the integrated CF, GL and GS problem, to efficiently design and operate CMSs. The model solves the integrated problem, taking into consideration intercellular and intracellular transportation times to determine the optimal cell formation, layout of machines and schedule of parts on the machines, simultaneously. Sequence-dependent set up times are also considered in the model. The performance of the model is tested by solving problems previously introduced in the literature considering two objectives; minimizing the makespan or minimizing the mean flow time in the system. The results show that the proposed model is efficient in solving small to medium-sized problems.

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

The increasing level of customer awareness and application of environmental laws by governments, on the one hand, and the increasing number of competitors, on the other hand, has obliged industry owners to include green activities in the design of the supply chain network. Green activities include any type of action that reduces environmental degradation. Hence, this study seeks to develop a bi-objective, mixed integer linear programming (MILP) model for designing a green supply chain network. In the proposed model, the minimisation of costs and detrimental environmental effects are discussed. LP metric method is used to solve the bi-objective model and the proposed model is run by using simulated data in small and medium sizes in GAMS software. Finally, the model sensitivity analysis is measured in order to evaluate the validity and performance of the proposed model by using demand-driven scenarios. The results of this model indicate the effectiveness of the proposed model.

Mixed Integer Linear Programming Model for Vehicle Routing Problem for Hazardous Materials Transportation

This paper presents a mixed integer linear programming (MILP) model to operate a dual source heat pump (DSHP) optimally. The DSHP draws environmental energy from an underground ice storage tank (IST) or wind infrared sensitive collectors (WISC). WISC are further used to regenerate the IST. Thermal energy storage (TES) stores the thermal output of the DSHP. A single-objective optimisation approach is applied to minimise the operational cost of the DSHP over a scheduling horizon of 24h. The developed framework is tested on various days, which are representative for each season of the year. Furthermore, it is investigated how the variable electricity price market data influence the dynamic behaviour of the DSHP.

Constructing the multi-energy complementary system with wind power, photovoltaic and concentrating solar power (CSP) as well as battery is an effective way to facilitate renewable energy integration. Coordinately optimization of battery and thermal energy storage capacities in CSP can help to reduce the investment cost and improve the renewable energy utilization. In this paper, a mixed integer linear programming model is formulated and system constraints such as renewable energy curtailment rate and operation constraints of each power plant are considered. The nonlinear thermoelectric conversion of CSP is approximated by the piecewise linearization method. Given the data of annual wind and solar power outputs as inputs, the optimal battery and thermal energy storage capacities which maximizes the equivalent uniform annual profit of system are obtained. Case studies are tested on a demonstration project of multi-energy complementary system in North China with a 300MW wind plant, 300MW photovoltaic solar station, 50MW CSP station and 50MW Li-ion battery. It is verified that the optimal capacities of battery and thermal energy storage are 194MWh and 1280MWht. In case studies, sensitivity of the system profit to battery capacity is also analyzed. The testing results validate the effectiveness of the proposed method.

This paper proposes a deterministic mixed integer linear programming model for the optimal operation of an energy system providing thermal and electrical energy for a residential and commercial nearly zero energy building. The space heating and space cooling demand of the buildings is simulated using a resistive-capacitive model within a quadratic program respectively. Thermal energy for space heating, space cooling and domestic hot water is buffered in thermal energy storage systems. A dual source heat pump provides thermal energy for space heating and domestic hot water, whereas space cooling is covered by an underground ice storage. The environmental energy sources of the heat pump are ice storage or wind infrared sensitive collectors. The collectors are further used to regenerate the ice storage. Further space heating demands are covered by a combined heat and power unit, which also produces electricity. Photovoltaic panels produce electrical energy which can be stored in a battery storage system. The electrical energy system is capable of selling and buying electricity from the public power grid. A mixed integer linear programming model is developed to minimise the operation cost of the combined commercial and residential nearly zero energy building over a scheduling horizon of 24h. The developed model is tested on two typical days, which are representative for the summer and winter season. Furthermore, it is investigated how external incentives such as varying electricity prices impact the optimal scheduling of the energy system.

Nowadays, intelligent vehicles have be used for transporting jobs in many manufacturing plants; thus, routing vehicles properly is very important for processing jobs. This paper studied a job shop scheduling problem considering intelligent vehicle transport. The problem is difficult to be handled because it needs to optimize the route of a vehicle and the processing sequences of jobs on machines simultaneously. To address this problem, a multi-attribute directed graph is proposed, in which a determined vehicle path associated with the attributes of job and machine is defined as a node and an undetermined vehicle path is defined as an arc; thereby, a mixed integer linear programming model is established. Numerical experiments show the effectiveness of the proposed model.

Due to booming economic development over the past decades, energy demands in most of China’s provincial power grids have increased sharply, and it has become challenging to guarantee the energy balance at peak periods. In many provincial electric systems of China, gas-fired generators are one of the most important peaking power sources to respond the load change at peak periods. To meet this practical necessity, a novel mixed integer linear programming model is proposed in this paper for the peak operation of gas-fired generating units with disjoint-prohibited operating zones. In the developed model, the objective function is chosen to minimize the peak-valley difference of the remaining load series that is obtained by subtracting the total generation of all the gas-fired units from the original load curve. The real-world simulations in several cases show that the developed model is able to generate satisfying scheduling results by reasonably allocating the power outputs of all the gas-fired generators in the scheduling horizon. Then, the management implications obtained lie in the fact that it is necessary to increase the share of peak power sources in the mid- to long-term planning of an electrical power system; and in the daily operation of the power grid, greater flexibility should be given to the gas-fired units to reduce peak pressure.

In this paper, a mixed integer linear programming model for the multi-item uncapacitated lot-sizing problem is presented. The considered factors for formulating the proposed model are the monthly demand of the selected product from the case study company, type of parts used in the product and their consumption coefficients based on the bill of materials, lead time to receive parts from outsourcing suppliers, the costs of ordering, purchasing, and holding, and the amount of safety stock for each part. Accordingly, several forecasting techniques are tried to determine future demands. The prices of the selected parts are estimated using linear regression method. The optimal safety stock for each part is calculated based on variance in demand, lead time and the target service level. Material requirements planning are also performed to obtain the economic purchasing schedule of parts. LINGO and GAMS software are used to solve and validate the suggested model respectively. The results show that the proposed model can find the optimum order quantity for each part per period, which minimises the total cost.

EIA-CNDP: An exact iterative algorithm for critical node detection problem

The integration of hybrid energy storage systems (HESS) in alternating current (AC) electrified railway systems is attracting widespread interest. However, little attention has been paid to the interaction of optimal size and daily dispatch of HESS within the entire project period. Therefore, a novel bi-level model of railway traction substation energy management (RTSEM) system is developed, which includes a slave level of diurnal HESS dispatch and a master level of HESS sizing. The slave level is formulated as a mixed integer linear programming (MILP) model by coordinating HESS, traction load, regenerative braking energy and renewable energy. As for the master level model, comprehensive cost study within the project period is conducted, with batteries degradation and replacement cost taken into account. Grey wolf optimization technique with embedded CPLEX solver is utilized to solve this RTSEM problem. The proposed model is tested with a real high-speed railway line case in China. The simulation results of several cases with different system elements are presented, and the sensitivity analyses of several parameters are also performed. The obtained results reveal that it shows significant economic-saving potentials with the integration of HESS and renewable energy.

In this paper we report two mixed integer linear programming models to resolve the malleable jobs scheduling problem with single resource. Jobs’ release dates and deadlines are taken into account. The total amount of available resource of the system is variable at different times. Numerical experimentation is conducted to evaluate the performance variability between two introduced models. The objective of this optimization problem is to minimize the total weighted completion time.

A risk management approach to the economic and environmental strategic design of ethanol supply chains