Sizing and Energy Management of Parking Lots of Electric Vehicles Based on Battery Storage with Wind Resources in Distribution Network

As the largest cement producer in Indonesia, PT Semen Gresik Group has a distribution network spread across West and the eastern of Indonesia and dominate the national cement market share of about 45%. To support the distribution of velocitythroughout the whole country, a strategic step to do is build a packing plant in the area of distribution. The results of the annual analysis, moving closer packing plant to the distribution area can be a solution for efficiency of the transportation costs up to 43%compared to the situation before, and it will be the company's long-term strategic plan. This research will be carried out optimization of the allocation of cement distribution network in the Kalimantan. Designing a distribution network allocation transshipmentmodel is the development of transport in linear programming models with the help of QM for Windows software. The expected output is the formation of an optimal distribution network with the minimum total cost. To determine the cost efficiency with the packing plant, the optimization model is made will be running four times as much computing. There are four scenarios used without removing the packing plant, with a packing plant in Banjarmasin, a packing plant in Banjarmasin and Pontianak, as well as a packing plant in Banjarmasin, Pontianak and Samarinda. The result of the four scenarios is known that with the packing plant can generate cost efficiencies by. By including the cost of investment in distribution and transportation costs in mind that in 2020, a third scenario could save Rp 127,229,422,000,-per year than it is without building a packing plant Rp 1,002,443,597,600 to Rp 850,446,425,600.

Shift to The Front II Komplek Sukamukti Banjaran Project is one of the projects implemented by one of the companies engaged in telecommunications. In its implementation, each project including Shift to The Front II Komplek Sukamukti Banjaran has a time limit specified in the contract. Project scheduling is an important role in predicting both the cost and time in a project. Every project should be able to complete the project before or just in the time specified in the contract. Delay in a project can be anticipated by accelerating the duration of completion by using the crashing method with the application of linear programming. Linear programming will help iteration in the calculation of crashing because if linear programming not used, iteration will be repeated. The objective function in this scheduling is to minimize the cost. This study aims to find a trade-off between the costs and the minimum time expected to complete this project. The acceleration of the duration of this study was carried out using the addition of 4 hours of overtime work, 3 hours of overtime work, 2 hours of overtime work, and 1 hour of overtime work. The normal time for this project is 35 days with a service fee of Rp. 52,335,690. From the results of the crashing analysis, the alternative chosen is to add 1 hour of overtime to 34 days with a total service cost of Rp. 52,375,492. This acceleration will affect the entire project because there are 33 different locations worked on Shift to The Front II and if all these locations can be accelerated then the duration of completion of the entire project will be effective

The paper presents use case simulations of fleets of electric buses in two cities in Europe, one with a warm Mediterranean climate and the other with a Northern European (cool temperate) climate, to compare the different climatic effects of the thermal management strategy and charging management strategy. Two bus routes are selected in each city, and the effects of their speed, elevation, and passenger profiles on the energy and thermal management strategy of vehicles are evaluated. A multi-objective optimization technique, the improved Simple Optimization technique, and a “brute-force” Monte Carlo technique were employed to determine the optimal number of chargers and charging power to minimize the total cost of operation of the fleet and the impact on the grid, while ensuring that all the buses in the fleet are able to realize their trips throughout the day and keeping the battery SoC within the constraints designated by the manufacturer. A mix of four different types of buses with different battery capacities and electric motor specifications constitute the bus fleet, and the effects that they have on charging priority are evaluated. Finally, different energy management strategies, including economy (ECO) features, such as ECO-comfort, ECO-driving, and ECO-charging, and their effects on the overall optimization are investigated. The single bus results indicate that 12 m buses have a significant battery capacity, allowing for multiple trips within their designated routes, while 18 m buses only have the battery capacity to allow for one or two trips. The fleet results for Barcelona city indicate an energy requirement of 4.42 GWh per year for a fleet of 36 buses, while for Gothenburg, the energy requirement is 5 GWh per year for a fleet of 20 buses. The higher energy requirement in Gothenburg can be attributed to the higher average velocities of the bus routes in Gothenburg, compared to those of the bus routes in Barcelona city. However, applying ECO-features can reduce the energy consumption by 15% in Barcelona city and by 40% in Gothenburg. The significant reduction in Gothenburg is due to the more effective application of the ECO-driving and ECO-charging strategies. The application of ECO-charging also reduces the average grid load by more than 10%, while shifting the charging towards non-peak hours. Finally, the optimization process results in a reduction of the total fleet energy consumption of up to 30% in Barcelona city, while in Gothenburg, the total cost of ownership of the fleet is reduced by 9%.

It is of great importance to identify the location of the harmonic sources for the harmonic governance in the power system. Applied with optimal measurement placement (OMP) and harmonic state estimation (HSE), this paper presents a novel process based on PMU measurements to locate the harmonic sources in the distribution network. Considering the cost and the observability, the OMP can provide a scheme of the measurement placement with the minimum number of PMU measurements. In order to simplify the HSE equation, the measured data are converted to the form of voltage by the method proposed in this paper.By solving the HSE equation, the location and magnitude of the harmonic source are evaluated. The methodology is applied to the IEEE 33-bus system, and the obtained results are properly analyzed.

Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.