Transmission network expansion planning based on coordinated economy and security

Expansion of the electricity sector and liberalized electricity market leads to more jobs challenging the designer of the transmission network. In order to match with the load growth and generation patterns schemes optimal transmission network expansion planning (TNEP) has to be done. It will find out where, when and how many new transmission lines should be added in the network. From the recent past, renewable energy resources are becoming major attraction part to the power sector. Large scale integration of distributed generation (DG) affects the electrical industry and as well as the strategy of the transmission network expansion. In this paper the impact of distributed generation (DG) in transmission network expansion planning (TNEP) is presented. The objective of TNEP is to minimize the transmission line investment cost required to meet the growing load and the added constraints. Static TNEP problem is modeled by using DC power flow model and to solve this problem a population search based algorithm named, Gbest Artificial Bee Colony (GABC) optimization algorithm is proposed. To show the effectiveness of the proposed method, it is evaluated on Garver's 6-bus network, and IEEE 24-bus test system. Results obtained are compared with the previous published literature.

Long term planning in power transmission network expansion provides a well ordered and profitable extension of power equipment and facilities to meet the expected electric energy demand with an allowable degree of reliability. However, high quality and improved reliability in energy supply have to be balanced with the available funds. The need to expand transmission network can never be over emphasized. Transmission Network Expansion Planning (TNEP) is a periodical measure that must be carried out due to dynamic societies that attract extra energy demands. It is highly important to minimize the network reinforcement and operational costs while satisfying the increase in demand imposed by technical and economic conditions over the planning horizon. Several optimization algorithms for TNEP problems have been developed and applied over the past decades. This paper presents a comprehensive state-of-the-art survey on the TNEP optimization algorithms. The approach of this paper is in the area of highlights of the various available TNEP algorithms, their applications, viability, computational complexities and drawbacks, which can aid in the identifications of the proper methods that can yield an optimal solution to TNEP problem.

The transmission network expansion planning is necessary for supplying the future needs, considering load growth. Furthermore, in restructured environments, transmission lines provide the required infrastructure for creating a competitive environment. In recent years, there has been a significant advancement in storage technologies. This advancement leads to using energy storage systems to postpone the construction or replacement of transmission lines. Therefore, in this paper, the problems of transmission expansion planning and energy storage systems deployment are investigated simultaneously. Considering the presence of storage devices and their effect on network operation cost, in this paper, the operation cost is modeled as an independent objective function along with investment cost. Moreover, the problems of transmission and storage expansion planning are modeled as a tri-objective optimization problem with the objectives of reducing costs and increasing the social welfare index in the power market. The multi-objective shuffled frog leaping evolutionary algorithm is used to solve these problems. The presented model for expansion planning is implemented and analyzed on IEEE 24-bus test system in the presence and absence of energy storage systems, and the effect of change in the price of energy storage systems is studied. The results of this research show that as the technology advances and the storage costs decrease, energy storage systems can play a pivotal role in reducing expansion planning costs of the power network and improving market-based indices in the restructured environment.

This paper proposes a new method for transmission network expansion planning (TNEP) with Multi-objective Memetic Algorithm (MOMA). Recently, the new environment such as power network liberalization and distributed generation brings about uncertainties in power system operation and planning. The importance of improving power supply reliability with probabilistic approaches is of main concern. This paper formulates TNEP as a multi-objective optimization problem that optimizes probabilistic reliability index as well as the construction cost to obtain a set of the Pareto solutions through Monte Carlo Simulation (MCS). This paper proposes a new method for TNEP with MOMA that combines Multi-objective meta-heuristics (MOMH) with Tabu Search (TS) to obtain better solution sets. The effectiveness of the proposed method is successfully demonstrated in the IEEE 24-bus system.

This paper proposes a new method for transmission network expansion planning (TNEP) with Multi-objective Memetic Algorithm (MOMA) in consideration of a probabilistic reliability index. Recently, power networks increase the degree of the uncertainties due to the new environment of power network liberations, the emergences of renewable energy, etc. As a result, the importance of improving power supply reliability with probabilistic approaches has been recognized in power system operation and planning. This paper formulates TNEP as a multi-objective optimization problem that optimizes a probabilistic reliability index as well as the construction cost to obtain a set of the Pareto solutions in Monte Carlo Simulation (MCS). This paper proposes a new method for TNEP with MOMA that combines Multi-objective meta-heuristics (MOMH) with Tabu Search (TS) to obtain better solution sets. MOMH is useful for evaluating a set of the Pareto solutions systematically while TS plays a key role to improve the solution quality. The effectiveness of the proposed method is successfully demonstrated in the IEEE 24-bus system.

A novel meta-heuristic model for the multi-year transmission network expansion planning

Guest Editorial: Situational awareness of integrated energy systems

Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning

The instability of energy resources and corresponding cost of the system are the main two problems for designing the hybrid solar-wind power generation systems. The configuration of the system must have a high reliability on the power supply availability but with a minimum cost. The purpose of this paper is to find the most optimum or balanced configuration between technical reliability and total annual cost for the PV module number, the wind turbine number, and the battery number. The appropriate strategy of load management is needed by adjusting the potential energy resource to the load power demand. Loss of Power Supply Probability (LPSP) is a method to determine the ratio of power generation unavailability by the system configuration which used as technical analysis. Annualized Cost of System (ACS) is a method to determine the total annualized cost of the project lifetime which used as economic analysis. The result from the simulation showed that the Differential Evolution (DE) algorithm can be an alternative method to find the best configuration with a low number of LPSP and ACS. Since DE has a better efficacy and faster time to find global optimum than other algorithms.

In deregulated power system, the transmission networks are experiencing significant changes in both the operational and expansion planning. Traditionally, transmission network expansion planning was based on minimization the total cost of energy delivering with a certainty level of reliability, and it is optimal under strict constraints. But some constraints and objectives are flexibility or elasticity due to uncertain in electricity market, the solution is insignificante in practice under strict constraints. This paper presents a new approach and model for transmission network expansion planning in deregulated power system. This method adopts elasticity constraints and elasticity coefficients for transmission network expansion planning, the main elastic factors node real power injection, circuit real power limitation and circuit investment cost are considered. The optimal solution is based on a certain belief level, it can overcome the shortcoming of strict constraints, and accords with the engineering practice. This Elaticity modelling method for transmission network expansion planning provides a new planning conceptual in deregulated power system.

This paper presents, a static transmission network expansion planning (STNEP) with considering the pumped-storage (PS) unit, and the transmission switching (TS) concept. The aim of this work, is to minimize the total cost (TC) of the system. The TC is the summation of the transmission line cost (CTL) and the generation cost (GC) of thermal generating units. TS is a relatively new area of research which provides a path to the transmission system operator (TSO) to avoid generation expansion and transmission expansion. The addition of new lines increases the investment cost hence, now-a-days more renewable energy resources have been adopted by the TSO. The proposed STNEP problem is modelled using a DC power flow model. The performance of the proposed scheme is tested on the modified Garver 6-bus system. The results indicate that the combinatorial effect of PS unit and TS has reduced the total cost of the system notably.

Transmission network expansion planning is a very critical problem due to not only the huge investment cost involved, but also the associated security issues. Any long range planning problem is confronted with the challenge of non-statistical uncertainty in the data. Although large number of papers have been published in this area, the efforts to tackle the above mentioned security and uncertainty issues have been relatively very few, due to the formidable complexity involved. This paper tries to bridge this gap by proposing a technique to tackle these problems. Boundary DC power flow is used to ascertain the worst power flows on the lines. A simple basic binary Genetic algorithm is used to solve the optimization problem as an illustration. Results for two sample test systems have been obtained to demonstrate the potential of the proposed method.

Background:
 An ambulance is a vehicle designed to be able to handle emergency patients, provide first aid and carry out intensive care while on the way to a referral hospital. Ambulance operations require a large amount of funds obtained from APBD funds through tariffs that were passed through the DKI Jakarta Governor Regulation five years ago. For this reason, a new tariff is required to adjust to current conditions.
 Objectives:
 The purpose of this study is to calculate the unit cost of ambulance services in DKI Jakarta to be a consideration in the tariff setting policy in DKI Jakarta province.
 Research Metodes: 
 This study uses a quantitative descriptive approach to obtain information about the unit cost of the Jakarta ambulance production unit. The method used is the calculation of real cost using the basis of the causes of costs. This research was conducted at the DKI Jakarta Emergency Ambulance using secondary data on investment costs, operational costs and maintenance costs in 2018.
 Results: 
 The total cost of emergency ambulance in 2018 is known that the proportion of three cost components, namely operational costs, is 76%, followed by investment costs of 20% and maintenance costs of 3%. The calculation of the total cost of medical evacuation using the double distribution method is Rp. 98,915,016,805.00 divided by the number of medical evacuations in 2018 of 37,564 activities, the unit cost of medical evacuation for the AGD of DKI Jakarta Health Office is Rp. 2,633,215.00 without subsidies. APBD costs, while if the subsidy component is included in the calculation, the unit cost for one trip to the AGD of the Health Office is Rp. 604,071.00. This is still far above the current tariff of Rp. 450.00, so the cost recovery rate (CRR) is still below. 100%.
 Conclusion: 
 From the three cost components consisting of investment, operational and maintenance costs,the largest proportion was operational costs at 76%. The Cost Recovery Rate has not reached 100% so that the existing rates have not covered the costs incurred.

Optimal integration of PV generators and D-STATCOMs into the electrical distribution system to reduce the annual investment and operational cost: A multiverse optimization algorithm and matrix power flow approach

A reliable methodology based on mine blast optimization algorithm for optimal sizing of hybrid PV-wind-FC system for remote area in Egypt