SMAA ‐Based FITradeoff : An Efficient Framework for Pairwise Elicitation in Multicriteria Decision Analysis

Battery energy storage system (BESS) can improve reliability with a reduced load of loss and reduce the uncertainty of photovoltaic (PV) to maintain a stable operating system in the power grid. BESS optimization refers to the sizing and siting of BESS, which is becoming more popular among consumers of cost-effectiveness, energy reduction, and demand cost. However, the use of retired batteries as BESS is a new challenge. Apart from solving the problem of battery disposal, it is also still used for providing service BESS, especially in terms of cost. Therefore, this study aims to deal with optimal sizing and siting BESS on a large power grid with retired battery or second-life battery to analyze energy shifting in reducing loss of load and PV peak shaving in the IEEE RTS-24 case study uses mixed integer linear programming (MILP) model. As a result, in the optimal BESS, retired batteries are placed on buses 6, 7, and 8, with a total capacity of 55.3 MW and 124.78 MWh. As a result, the optimal BESS can improve reliability by reducing PV curtailment costs by 66% and loss of load from cases without BESS. Moreover, BESS optimal can reduce cost BESS by 13.63% and total operation cost by 0.4% lower than case BESS without optimal.

In the scope of MAVT (Multi-Attribute Value Theory), one of the most difficult tasks is the elicitation of criteria scaling constants of an additive model for the aggregation of criteria. That might be the reason why there are so many MCDM/A (Multi-Criteria Decision Making/Aiding) methods, among which is the FITradeoff (Flexible and Interactive Tradeoff) method, which has been developed precisely to meet this challenge. One of its advantages is that it uses partial information about the preferences of a Decision Maker (DM). This requires less effort from the DM, since this method makes comparisons of consequences (or outcomes) based on strict preference rather than on indifference, which is what the traditional tradeoff procedure does. Two case studies are presented using the FITradeoff method: a supplier selection problem and a facility location problem. Using a Decision Support System of FITradeoff for the decision process, the flexibility of this process is analyzed, in order to determine the best one in a specified set of alternatives, or even to rank them.

Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation

Crop planting structure optimization is a significant way to increase agricultural economic benefits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic profits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study, three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming (IFCCP) model and an inexact fuzzy linear programming (IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimizationtheory-based fuzzy linear multi-objective programming model was developed, which is capable of reflecting both uncertainties and multi-objective. In addition, a multiobjective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic benefits and the denominator representing minimum crop planting area allocation. These models better reflect actual situations, considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in Minqin County, north-west China. The advantages, the applicable conditions and the solution methods of each model are expounded. Detailed analysis of results of each model and their comparisons demonstrate the feasibility and applicability of the models developed, therefore decision makers can choose the appropriate model when making decisions.

Within the deregulation process of distribution systems, the distribution locational marginal price (DLMP) provides effective market signals for future unit investment. In that context, this paper proposes a two-stage stochastic bilevel programming (TS-SBP) model for investors to best allocate battery energy storage systems (BESSs). The first stage obtains the optimal siting and sizing of BESSs on a limited budget. The second stage, a bilevel BESS arbitrage model, maximizes the arbitrage revenue in the upper level and clears the distribution market in the lower level. Karush-Kuhn-Tucker (KKT) optimality conditions, strong duality theory, and the big-M method are utilized to transform the TS-SBP model into a tractable two-stage stochastic mixed-integer linear programming (TS-SMILP) model. A novel statistics-based scenario extraction algorithm is proposed to generate a series of typical operating scenarios. Then, scale reduction strategies for BESS candidate buses and inactive voltage constraints are proposed to reduce the scale of the TS-SMILP model. Finally, case studies on the IEEE 33-bus and 123-bus systems validate the effectiveness of the DLMP in incentivizing BESS planning and the efficiency of the two proposed scale reduction strategies.

This study has been undertaken to develop a consumer-oriented feasibility method for a hybrid photovoltaic (PV)-battery energy storage (BES) system by analyzing a real life house in Istanbul, Turkey, as a case study. The hourly electricity demand of the house was estimated by carrying out a detailed survey of the life style and daily habits of the household. No algorithm of any kind was used for the estimation of the energy demand with the exception of relating the lighting requirement to the daylight hours and the heating and cooling requirements to the seasonal weather changes. The developed method estimates the annual demand with an overall error of 8.68%. The net grid dependency and the feasibility of the PV-BES system was calculated for different combinations of PV and BES system sizes. It was found that when the maximum available roof area is used for PV installation and when the BES system size is increased, it is possible to achieve almost zero net grid dependency, and it is estimated that houses that are in regions with more abundant solar radiation and/or with lower annual electricity consumption, can reach zero net grid dependency. However, the feasibility indicator, which is the payback period, turned out to be no less than 25 years in any of the scenarios. The reasons for the infeasibility are the high prices of PV and BES systems as well as the current restriction in the regulations in Turkey, which prevents BES system owners from participating in unlicensed energy generation schemes and selling excess electricity back to the grid. In order to overcome this situation, regulations should be updated to allow BES system owners to benefit from feed-in-tariff schemes, thereby increasing the popularity of both PV and BES usage in Turkey.

The integration of an energy storage system into an integrated energy system (IES) enhances renewable energy penetration while catering to diverse energy loads. In previous studies, the adoption of a battery energy storage (BES) system posed challenges related to installation capacity and capacity loss, impacting the technical and economic performance of the IES. To overcome these challenges, this study introduces a novel design incorporating a compressed CO2 energy storage (CCES) system into an IES. This integration mitigates the capacity loss issues associated with BES systems and offers advantages for configuring large-scale IESs. A mixed integer linear programming problem was formulated to optimize the configuration and operation of the IES. With an energy storage capacity of 267 MWh, the IES integrated with a CCES (IES–CCES) system incurred an investment cost of MUSD 161.9, slightly higher by MUSD 0.5 compared to the IES integrated with a BES (IES–BES) system. When not considering the capacity loss of the BES system, the annual operation cost of the IES–BES system was 0.5 MUSD lower than that of the IES–CCES system, amounting to MUSD 766.6. However, considering the capacity loss of the BES system, this study reveals that the operation cost of the IES–BES system surpassed that of the IES–CCES system beyond the sixth year. Over the 30-year lifespan of the IES, the total cost of the IES–CCES system was MUSD 4.4 lower than the minimum total cost of the IES–BES system.

Economic battery sizing and power dispatch in a grid-connected charging station using convex method

This paper proposes a real‐time co‐optimisation framework integrated with automatic generation control (RTC‐AGC) for the optimal reallocation of energy and regulation reserves in real‐time electricity markets. A rolling‐horizon optimisation approach is also proposed to dynamically optimise resource scheduling by reallocating based on forecasted load demand and renewable generation patterns over a moving time window. Inverter‐based battery energy storage (IBES) systems are also introduced as flexible resources to regulate frequency deviation in AGC by optimally reallocating up‐ and down‐regulation reserves in the real‐time market. The integrated RTC‐AGC framework accurately represents the dynamic behaviour of thermal generation units and IBES systems, enabling the precise and cost‐efficient reallocation of up‐ and down‐regulation reserves in the real‐time market. The proposed model is formulated as a two‐stage stochastic mixed‐integer linear programming model and solved by the CPLEX solver in GAMS software. The findings highlight that IBES can significantly reduce frequency deviations by 50% while lowering operational costs by 7.13% in power systems integrated with renewable energy resources.

A fuzzy inhomogenous multiattribute group decision making approach to solve outsourcing provider selection problems

Rolling schedule approaches for supply chain operations planning

In this paper, the appropriate rated power of battery energy storage system (BESS) and the operating limit capacity of wind farms are determined considering power system stability, and novel output control methods of BESS and wind turbines are proposed. The rated power of BESS is determined by correlation with the kinetic energy that can be released from wind turbines and synchronous generators when a disturbance occurs in the power system. After the appropriate rated power of BESS is determined, a novel control scheme for quickly responding to disturbances should be applied to BESS. It is important to compensate the insufficient power difference between demand and supply more quickly after a disturbance, and for this purpose, BESS output is controlled using the rate of change of frequency (ROCOF). Generally, BESS output is controlled by the frequency droop control (FDC), however if ROCOF falls below the threshold, BESS output increases sharply. Under this control for BESS, the power system’s stability can be improved and the operating limit capacity of wind farms can be increased. The operating limit capacity is determined as the smaller of technical limit and dynamic limit capacity. The technical limit capacity is calculated by the difference between the maximum power of the generators connected to the power system and the magnitude of loads, and the dynamic limit capacity is determined by considering dynamic stability of a power system frequency when the wind turbines drop out from a power system. Output of the dynamic model developed for wind turbine is based on the operating limit capacity and is controlled by blade pitch angle. To validate the effectiveness of the proposed control method, different case studies are conducted, with simulations for BESS and wind turbine using Power System Simulation for Engineering (PSS/E).

This paper addresses design consideration on Battery Energy Storage System (BESS) sizing of Hybrid Electric Marine Vessel. Different size of BESS is simulated to assess fuel consumption of the vessel, charge/discharge peak power of the BESS, and equivalent life cycle of the BESS used. To determine power contribution between generators and BESS, a Power Management System (PMS) is designed based on load demand and battery remaining energy. In this paper, a ferry's load profile is used as the case study. From simulation results, there is not much reduction of fuel consumption when the BESS size is over 200kWh. However, the benefits of having larger BESS size are smaller required ratio of peak power to rated energy of the BESS and smaller equivalent life cycle used. Furthermore, the minimum BESS size is then determined whether the required ratio of peak power to rated energy can be met by commercially available battery. Different chemistries of lithium-ion based BESS technology are considered in this paper.

This paper presents a broad overview of main contributions related to the FITradeoff (Flexible and Interactive Tradeoff) method. FITradeoff is a multicriteria method developed within the scope of the Multiattribute Value Theory (MAVT), considering partial information from the decision maker (DM) in the preference modelling process. Over the last few years, several methodological developments on this method have been published in the literature, as well as practical applications to a wide range of multicriteria decision problems. The most recent methodological advances are related to preference modelling process, which now integrate the two paradigms of elicitation by decomposition and holistic evaluation. Furthermore, contributions from behavioral studies, some of them including decision neuroscience, have enhanced the DSS free available for FITradeoff. In this paper, all previously developed works related to the FITradeoff method are approached, considering both methodological developments and practical applications. A summary on the different modeling approaches for solving different decision problematics (choice, ranking, sorting and portfolio) with FITradeoff is presented. The recently proposed combination of preference modeling paradigms - elicitation by decomposition and holistic evaluation - within the FITradeoff decision process is explained, as well its potential advantages for the elicitation process. Moreover, a brief review on the practical applications of the method in different contexts is presented. In addition, this work also brings a summary on the results of behavioral experiments conducted using neuroscience tools with the FITradeoff method, as well the methodological insights resulted from them, and future perspectives of potential research topics related to the FITradeoff method.

Unit commitment for multi-terminal VSC-connected AC systems including BESS facilities with energy time-shifting strategy