• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.547-554
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• 2016

Analyze the customer daily load patterns, be used to determine the optimal charging and discharging schedule which can minimize the electrical charges through the battery energy storage system(BESS) installed in consumers is an object of this paper. BESS, which analyzes the load characteristics of customer and reduce the peak load, is essential for optimal charging and discharging scheduling to save electricity charges. This thesis proposes optimal charging and discharging scheduling method, using particle swarm optimization (PSO) and penalty function method, of BESS for reducing energy charge. Since PSO is a global optimization algorithm, best charging and discharging scheduling can be found effectively. In addition, penalty function method was combined with PSO in order to handle many constraint conditions. After analysing the load patterns of target BESS, PSO based on penalty function method was applied to get optimal charging and discharging schedule.

• Journal of the Korean Institute of Navigation
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• v.14 no.1
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• pp.21-38
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• 1990

This paper discusses the various modes of operations of cargo ships which are liner operations, tramp shipping and industrial operations, and mathematical programming, simulation , and heuristic method that can be used to solve ships routing and scheduling problems for each of these operations. In particular, this paper put emphasis on a crude oil tanker scheduling problem. The problem is to achieve an optimal sequence of cargoes or an optimal schedule for each ship in a given fleet during a given period. Each cargo is characterized by its type, size, loading and discharging ports, loading and discharging dates, cost, and revenue. Our approach is to enumerate all feasible candidate schedate schedules for each ship, where a candidate schedule specifies a set of cargoes that can be feasibly carried by a ship within the planning horizon , together with loading and discharging dates for each cargo in the set. Provided that candidate schedules have been generated for each ship, the problem of choosing from these an optimal schedule for each ship is formulated as a set partitioning problem, a set packing problem, and a integer generalized network problem respectively. We write the PASCAL programs for schedule generator and apply our approach to the crude oil tanker scheduling problem similar to a realistic system.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2227-2235
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• 2018

Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.71-89
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• 2002

In this paper, an integrated planning system is introduced for the efficient operation of container terminal. It consists of discharging and loading planning, yard planning, and berth scheduling subsystem. This interface of this system is considered for user's convenience, and the rule-based system is suggested and developed in order to make planning with automatic procedures, warning functions for errors.

• Journal of Communications and Networks
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• v.14 no.6
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• pp.662-671
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• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.103-113
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• 2012

A yard in a container terminal is a temporary storage space before containers are loaded onto the target vessel or delivered to consignees. For improving the utilization of the space in the yard and the efficiency of loading and discharging operations, it is important that operation plans must be carefully constructed in advance. A heuristic method is suggested to solve operation space planning problems considering workloads on handling equipment as well as space availabilities. The operation plans in this paper includes quay crane (QC) schedules and space plans for multiple vessels considering the workload in the container yard of container terminals. This paper evaluates the effectiveness of a space planning method and the performance of a new QC scheduling method using a simulation model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.21-28
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• 2013

The paper proposes an optimal sizing method of a customer's battery energy storage system (BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost. The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost saving curves via capacity of BESS. Case study uses real data from an apartment-type factory customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.157-163
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• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 120 buildings that KEPCO headquarter and branch offices use. K-BEMS system is composed of PV, battery, and hybrid PCS. In this system, ESS, PV, lighting is used to save building energy based on demand prediction. Currently, neural network technique for short past data is applied to demand prediction, and fixed scheduling method by operator for ESS charging/discharging is used. To enhance this system, KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. As the result of this project, we developed new real-time highly reliable building demand prediction technique with error free and optimized automatic ESS charging/discharging technique. Through several field test, we can certify the developed algorithm performance successfully. So we will describe the details in this paper.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.6
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• pp.511-521
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• 2014

This study proposes an electric vehicle (EV) smart panel board and its control method on the basis of charging scheduling. The proposed system consists of batteries, a three-phase battery charger, three single-phase inverters, transfer switches for electric power distribution, and a controller. The three-phase battery charger usually charges the batteries at midnight when electric rates are cheap and in light load. When the electric power consumption of the EV standard chargers connected to one phase of the power line is relatively large or when a blackout occurs, the electric power stored in the battery is supplied by discharging through the inverters to the EV standard chargers. As a result, the value of peak load and the charging electric power quantity supplied from a utility grid are reduced, and the current unbalance is improved. The usefulness of the proposed system is confirmed through simulations, experiments, and case studies.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.12
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• pp.638-644
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• 2017

Optimal cooling operation algorithm was developed based on a simulation case of a single family house model equipped with renewable energy facility. EnergyPlus simulation results were used as virtual test data. The model contained three energy storage elements: thermal heat capacity of the living room, chilled water storage tank, and battery. Their charging and discharging schedules were optimized so that daily electricity bill became minimal. As an optimization tool, linear programming was considered because it was possible to obtain results in real time. For its adoption, EnergyPlus-based house model had to be linearly approximated. Results of this study revealed that dynamic cooling load of the living room could be approximated by a linear RC model. Scheduling based on the linear programming was then compared to that by a nonlinear optimization algorithm which was made using GenOpt developed by a national lab in USA. They showed quite similar performances. Therefore, linear programming can be a practical solution to optimal operation scheduling if linear dynamic models are tuned to simulate their real equivalents with reasonable accuracy.