Optimal Contract Capacity Research Articles

Contracted Capacity Optimization Problem of Industrial Customers with Risk Assessment

This study developed a risk assessment tool for contract capacity optimization problems using the ant colony optimization and auto-regression model. Based on the historical data of demand consumption, the Least Square algorithm, the Recursive Levinson–Durbin algorithm, and the Burg algorithm were used to derive the auto-regression model. Then, ant colony optimization was used to search for the auto-regression model’s best p-order parameters. To avoid the risk of setting the contract capacity, this paper designed the risk tolerance parameter β to correct the predicted value of the auto-regression model. Ant colony optimization was also used to search for the optimal contract capacity with risk assessment under the two-stage time-of-use and three-stage time-of-use. This study employed an industrial consumer with high voltage power in Taiwan as the research object, used the AR model to estimate the contract capacity under the risk assessment, and cut back electricity usage to reduce the operation cost. The results can be used as a basis to develop an efficient tool for industrial customers to select contract capacities with risks to obtain the best economic benefits.

Optimal Configuration with Capacity Analysis of PV-Plus-BESS for Behind-the-Meter Application

As the cost of photovoltaic (PV) systems and battery energy storage systems (BESS) decreases, PV-plus-BESS applied to behind-the-meter (BTM) market has grown rapidly in recent years. With user time of use rates (TOU) for charging and discharging schedule, it can effectively reduce the electricity expense of users. This research uses the contract capacity of an actual industrial user of 7.5 MW as a research case, and simulates a PV/BESS techno-economic scheme through the HOMER Grid software. Under the condition that the electricity demand is met and the PV power generation is fully used, the aim is to find the most economical PV/BESS capacity allocation and optimal contract capacity scheme. According to the load demand and the electricity price, the analysis shows that the PV system capacity is 8.25 MWp, the BESS capacity is 1.25 MW/3.195 MWh, and the contract capacity can be reduced to 6 MW. The benefits for the economical solution are compared as follows: 20-year project benefit, levelized cost of energy (LCOE), the net present cost (NPC), the internal rate of return (IRR), the return on investment (ROI), discounted payback, total electricity savings, renewable fraction (RF), and the excess electricity fraction. Finally, the sensitivity analysis of the global horizontal irradiation, electricity price, key component cost, and real interest rate will be carried out with the most economical solution by analyzing the impacts and evaluating the economic evaluation indicators. The analysis method of this research can be applied to other utility users to program the economic benefit evaluation of PV/BESS, especially an example for Taiwan’s electricity prices at low levels in the world.

An enhanced stochastic algorithm for optimal contract capacity of furniture stores

This paper integrated the Grey Theory and Particle Swarm Optimization to propose an Enhanced Stochastic Algorithm (ESA) for dealing with the optimal contact capacity of the furniture store. ESA is used to find the minimal total annual electricity bill and simultaneously find the optimal contract capacity under the operational and system’s constraints. The historical demand for a furniture store is first collected in this paper. Grey theory is used to predict the next year demand of every month. Based on the Time-Of-Use (TOU) rates employed by the Taiwan Power Company (TPC), the annual electric bill including basic charge and penalty charge is derived to formulate an objective function. Particle Swarm Optimization (PSO) was adopted to minimize the annual electricity bill and simultaneously find the optimal contract capacity. Practical load consumption data were used to prove the validity of the ESA. Users can able to estimate the contract capacity by themselves and curtail the electricity cost.

Multisources of Energy Contracting Strategy with
 an Ecofriendly Factor and Demand Uncertainties

This study presents a mathematical formulation to optimize contracting capacity strategiesof multisources of energy for institutional or industrial consumers considering demand uncertainties.The objective consists of minimizing the total costs composed of the different types of energy contractcapacity costs, penalty price, and an ecofriendly factor. The penalty price is charged on the demand ofenergy exceeding the total contract capacities. The ecofriendly factor encourages the use of renewableenergy and reduces the traditional energy used in the optimal mix of energy sources. The proposedmodel is tested based on demand of energy inspired from real data. These numerical experiments areanalyzed to illustrate the impact of encouraging the use of renewable energy sources by introducingthe ecofriendly factor and the influence of penalty price and uncertainty in the demand of energy.The results show that in the presence of low penalty price or low uncertainty a large amount ofecofriendly support is needed for using more renewable energy sources in the optimal contractcapacity combination.

Optimal Load Management in a Shipyard Drydock

With the proliferation of enabling smart grid technologies, industries are increasingly looking to reduce their electricity costs through the adoption of renewable energy sources and efficient load management strategies. In this context, the realization of lower electricity costs requires the design of efficient energy management systems (EMSs). An EMS needs to factor in the unique operational requirements of the industry for which it is designed. Consequently, there has been a lot of research interest in designing EMSs for various industrial applications. However, the existing EMS formulations and models are not suitable for a shipyard drydock. Shipyard drydocks may be treated as grid-connected microgrids containing pump loads, interruptible loads, critical loads and heterogeneous generation sources. This paper proposes three modules, which can constitute a shipyard drydock energy management system (SEMS). A load forecasting module generates short term and medium term load forecasts using historical load demand data and ship arrival schedules as inputs. A multilayer feed-forward neural network with backpropagation is used to perform the load forecasting. A contracted capacity optimization module uses the medium term load forecast to find the optimal contracted capacity for the drydock. The short term load forecast and the optimal contracted capacity are used by an optimal scheduling module to reduce the total electricity cost incurred by the shipyard drydock. Case studies performed using data from a local shipyard demonstrate the efficacies of the proposed load forecasting and optimal scheduling modules in improving the accuracies of the load forecasts and reducing the overall electricity cost, respectively.

Dual sourcing for mitigating humanitarian supply chain disruptions

Purpose – Proactive planning strategies for “slow-onset” disruptions that affect humanitarian supply chains (SC) developed to address chronic pressing societal problems, can have a significant impact on boosting the operational and financial performance of these chains. The purpose of this paper is to develop a methodology that quantifies the impact of a risk mitigation strategy widely employed in commercial SCs, namely emergency sourcing (ES), on the performance of humanitarian SCs taking into account backorders’ clearance time, unsatisfied demand, and cost. Design/methodology/approach – Discrete event simulation is employed in order to evaluate alternative ES strategies based on a total cost criterion, which incorporates inventory-related costs, as well as premium contract costs paid for emergency replenishment. Backorders’ clearance time and time-to-recovery are also employed as a design parameters. Findings – The results document the significant impact of disruptions on expected total cost, and the beneficial role of ES in hedging against disruptions. To that end, the proposed methodology determines the optimal emergency contracted capacity for a given premium, or alternatively the maximum premium cost value that ensures the feasibility of the implemented ES strategy in the long-run, along with the associated cost and time savings, and reduction of the unsatisfied demand. Originality/value – The fundamental objective is to provide a decision-making support methodology for deciding on whether to implement an ES strategy or not in humanitarian SCs, and the level of the optimal contracted reserved capacity. The results could be of great value to aid providers, policy-makers, and regulators.

Wind-photovoltaic capacity coordination for a time-of-use rate industrial user

As the capacity of wind and photovoltaic (PV) generation systems increases, wind-PV capacity coordination for a time-of-use (TOU) rate industrial user may become an important problem. This coordination can maximise the economic benefits of investing in a wind generation system and a PV generation system. An evolutionary particle swarm optimisation approach to solve the wind-PV capacity coordination for a TOU rate industrial user is proposed. A benefit-cost ratio (BCR) is used to evaluate the economic benefit of investing in wind and PV generation systems for a TOU rate industrial user. The optimal contract capacities and the optimal installed capacities of the wind and PV generation systems for a TOU rate industrial user are obtained. The BCR of investing in wind and PV generation systems are maximised. Test results illustrate the merits of the proposed approach and help determine the impact of changes in electricity cost and capital cost on wind-PV capacity coordination for a TOU rate industrial user.

Iteration particle swarm optimization for contract capacities selection of time-of-use rates industrial customers

This paper presents a new algorithm for solving the optimal contract capacities of a time-of-use (TOU) rates industrial customer. This algorithm is named iteration particle swarm optimization (IPSO). A new index, called iteration best is incorporated into particle swarm optimization (PSO) to improve solution quality and computation efficiency. Expanding line construction cost and contract recovery cost are considered, as well as demand contract capacity cost and penalty bill, in the selection of the optimal contract capacities. The resulting optimal contract capacity effectively reaches the minimum electricity charge of TOU rates users. A significant reduction in electricity costs is observed. The effects of expanding line construction cost and contract recovery cost on the selection of optimal contract capacities can also be estimated. The feasibility of the new algorithm is demonstrated by a numerical example, and the IPSO solution quality and computation efficiency are compared to those of other algorithms.

Optimal Energy Management Integration for a Petrochemical Plant Under Considerations of Uncertain Power Supplies

The electric power demands of many petrochemical plants are matched by supplies from an in-house cogeneration system and from the electric grid. However, due to the fluctuations of fuel costs, production, and electricity rates, it is necessary to balance electric supply between these two sources. In reality, uncertain effects play a very important role in this decision-making problem. One of the most important uncertainties is the occurrence of power interruptions from either one of the supply sources, which could endanger operability and reliability of plant operations. To minimize the total energy cost under consideration of unexpected power failures, we break up the solution of the problem into two layers. The outer layer is to determine the optimum contracting of three-section time-of-use rate. We use an artificial neural network regression model as a meta-model to simulate the contour plot of a nonconvex cost function. The occurrences of incidental power failures are simulated by the Monte Carlo method. The inner layer is to determine the optimum operation of the cogeneration system. Since the searching space is huge in the outer layer and the Monte Carlo simulation in the inner layer is time consuming, we implement an interactive sampling search approach to find the optimal contract capacity in this multi-local-optima problem.

Optimal Contract Capacities for Time-of-Use Rate Industrial Customers Using Stochastic Search Algorithms

This paper presents two stochastic search algorithms for optimizing the contract capacities of time-of-use (TOU) rate industrial customers. The proposed genetic algorithm (GA) and evolutionary algorithm (EA) are used to search the optimal contract capacities for the TOU rate customers to minimize the summation of demand and penalty charges. Because of the global search capabilities of the GA and EA, the proposed algorithms can optimize the solutions of the complicated, nondifferentiable, optimal contract capacity problem. The proposed algorithms have been tested on a three-section TOU rate industrial customer of Taipower system and are compared with the existing method. The test results demonstrate that the proposed algorithms can efficiently optimize the contract capacities for TOU rate customers.

Optimal contracts decision of industrial customers

This paper develops a software package to calculate the optimal contract capacities for industrial customers. Based on the time-of-use (TOU) rates employed by the Taiwan Power Company, the objective function is formulated, to minimize the electricity bill of industrial customers during the whole year period. Evolutionary programming (EP) was adopted to solve this problem. Users can get the optimal contract capacities for the peak load, semi-peak load, and off-peak load, respectively. Practical load consumption data were used to prove the validity of this program. Results show that the software developed in this paper can be used as a useful tool for industrial customers in selecting contract capacities to curtail the electricity bill.

Determination of optimal contract capacities and optimal sizes of battery energy storage systems for time-of-use rates industrial customers

This paper sets up a formulation to determine the optimal contract capacities and optimal sizes of battery energy storage systems for time-of-use rate customers. The advanced multi-pass dynamic programming and expert knowledge base rules are then applied to solve this problem. Two industrial customers of the Taiwan Power Company System are used as examples to test this algorithm. Results show that the optimal sizes of battery energy storage systems and the optimal contract capacities of customers during the life cycle of battery energy storage systems can be achieved by this algorithm; therefore the maximum economic benefits of battery energy storage systems for time-of-use rate customers can be estimated. >