Multi-objective modelling of day ahead scheduling of MMG-based distribution networks accompanied by DEA considering economics,emissions and power quality

ABSTRACT

This paper presents the state of the art relating to multi-objective modelling for day ahead scheduling of multi micro grid-based distribution networks, using optimal power flow (OPF) accompanied by data envelopment analysis (DEA). In this paper eco-reliability cost function, power quality enhancement and emission reduction are treated as the objective functions and the uncertainties of renewable distributed generations (DGs), load demand and market price are incorporated into the problem. This method is able to find the optimum operation of DGs in grid-connected or isolated MGs, power transaction between each MG and upstream networks/other MGs and hourly reconfiguration instants. For this purpose, firstly OPF is applied to the problem, then the obtained optimal solutions are prioritised by DEA and ranking is done, based on the efficiencies of the optimal solutions. Finally, the provided results validate the practicability of the proposed method and accuracy of the outcomes.     

Uncertainty in peak cooling load calculations

Peak cooling loads are usually calculated at early stages of the building project, when large uncertainties affect the input data. Uncertainties arise from a variety of sources like the lack of information, random components and the approximate nature of the building mathematical model. Unfortunately, these uncertainties are normally large enough to make the result of the calculation very dependent on starting assumptions about the value of input data. HVAC engineers deal with uncertainties through worst-case scenarios and/or safety factors. In this paper, a new approach is proposed based on stochastic simulation methods. Uncertainty bands are applied to the input data and propagated through the building model in order to determine their impact on the peak cooling load. The result of this calculation is a probability distribution that quantifies the whole range of possible peak loads and the probability of each interval. The stochastic solution is compared with the conventional one, and a global sensitivity analysis is undertaken to identify the most important uncertainties.     

Evaluation of maximum allowable capacity of distributed generations connected to a distribution grid by dual genetic algorithm

This paper proposes a dual genetic algorithm based approach to evaluate the maximum allowable capacity of distributed generations (DGs) connected to a distribution grid. The uncertainties in the existing deterministic approaches for evaluating the steady-state voltage deviation due to distributed generation are discussed as well. Nowadays, deterministic approaches are widely adopted by those who propose the interconnection of DGs. However, the existing deterministic approaches overlook some operation conditions that may give rise to an incorrect result and lead to a wrong decision in practical applications. In this paper, various factors affecting steady-state voltage deviation are discussed first. Then, a maximum allowable DG capacity evaluation approach based on the dual genetic algorithm is proposed. Finally, the uncertainties of the existing deterministic approaches are discussed. It is intended as reference for utility engineers processing DG interconnection applications.     

Optimal microgrid programming based on an energy storage system,price-based demand response,and distributed renewable energy resources

Microgrids (MGs) in distribution systems can be operated in far regions at lower investment costs using renewable distributed energy resources (DERs). The present paper introduces a stochastic model for optimal energy-heat programming and the daily storage of an MG. Bi-level stochastic programming is presented for integrated energy-heat scheduling and storage in the presence of an energy storage system (ESS) and demand response (DR) based on social welfare maximization. Out of the incentive-based DR programs, the tender and redemption and the ancillary services market programs were selected and applied to the given model. Besides, the time of use (TOU) -based DR and real-time pricing (RTP) were considered as the price-based demand response (PBDR) programs in optimal programming. The PBDR programs have been included in the objective function using a linear function based on consumer benefits. The proposed bi-level stochastic model was solved using a developed metaheuristic optimization algorithm called the lightning search algorithm (LSA) in the present work. The Latin hypercube sampling (LHS) and KMEANS methods were used to produce and reduce the scenario. The proposed framework was investigated in a 33-bus test model. The obtained simulation results were evaluated from different aspects. The TOU and RTP effects and ESS are shown in obtained numerical analysis by considering the operating cost, total social welfare, and the client's utility function.     

A critical review of Korea's long-term contract for renewable energy auctions: The relationship between the import price of liquefied natural gas and system marginal price

The aim of Korea's Renewable Portfolio Standard (RPS) and newly adopted auction system with a long-term fixed-price renewable contract was to support renewable energy providers in hedging electricity and Renewable Energy Certificate (REC) price risks. This study found a long-run positive relationship between the import price of Liquefied Natural Gas (LNG) and System Marginal Price (SMP) in Korea, demonstrating that the fluctuations in global fuel prices are likely to increase uncertainties in renewable investments. The fixed-price contract, which cannot be revised once determined, encourages renewable energy providers' speculative decision-making, and the uncertainty caused by the contract system may discourage investment decision-making.     

Stochastics multi-objective modelling of simultaneous reconfiguration of power distribution network and allocation of DGs and capacitors

This paper suggests a method based on stochastic multi-objective modelling for optimally siting and sizing of distributed generations and capacitors and simultaneously network reconfiguration, in order to take more advantages of recent developed technologies. With respect to the widespread effect of uncertainties of load forecasting and market price, some strategies must be devised so as to well incorporate them into the problem. The mentioned problem can be regarded as a multi-objective optimisation problem for which NSGA-II accompanied by fuzzy set theory are chosen. The main aim of DisCo is to provide highly reliable power at the lowest possible costs. Besides, voltage deviation and voltage stability as the criteria of power quality in distribution networks and emission pollutant reduction are treated as independent objective functions. Eventually, to validate the effectiveness of the proposed scheme, simulations are carried out and the results are compared to the initial configuration.     

不确定组合系统的分散鲁棒输出跟踪控制

就一类满足匹配条件的含有时变不确定性的大系统,研究了分散鲁棒跟踪控制器的设计问题.笔者利用Riccati 方程方法给出了线性分散控制律,使得不确定的每个子系统都能鲁棒跟踪某一相应的动态参考输入．因为不确定性为时变的不能得到渐近跟踪,但可以借助前馈控制方式使跟踪误差任意小,从而达到实际跟踪的目的．文中的方法简便有效,对状态方程中的时变未知干扰能够很好地抑制,并通过例子加以验证.     

水平跨越式热水供暖系统的设计及电算程序

水平跨越式热水供暖系统与水平顺流式热水供暖系统不同，因此，设计时不能采用同一计算方法，文中通过分析，找出了水平跨越式热水供暖系统散热器支管与跨越管之间的流量分配关系。     

Optimal planning of distributed generation with application of multi-objective algorithm including economic,environmental and technical issues with considering uncertainties

This paper proposes a stochastic multi-objective model for integration of distributed generations (DGs) in distribution networks. The proposed model determines the optimal location and size of DGs by optimising different objective functions dependently and simultaneously subject to the operating constraints. If proper sizes of DGs are located in suitable sites and are also managed properly they can improve integrity, reliability and efficiency of the system. Regarding the widespread impact of uncertainties, some strategies must be devised in order to incorporate them well into power system modelling and hence achieve the best possible strategy to be adopted which its characteristics keep closer to reality. The most important uncertainties in network planning are load forecasting and market price errors. The proposed scheme is solved using non-dominated sorting genetic algorithms II, allowing the distribution company (DisCo) to exercise his/her personal preferences. To validate the effectiveness of the proposed scheme, simulations are carried out on a 33-bus distribution network and finally the attained results are discussed.     

Direct estimation of quantile functions using the maximum entropy principle

The paper presents a distribution free method for estimating the quantile function of a non-negative random variable using the principle of maximum entropy (MaxEnt) subject to constraints specified in terms of the probability-weighted moments estimated from observed data. Traditionally, MaxEnt is used for estimating the probability density function under specified moment constraints. The density function is then integrated to obtain the cumulative distribution function, which needs to be inverted to obtain a quantile corresponding to some specified probability. For correct modelling of the distribution tail, higher order moments must be considered in the analysis. However, the higher order (>2) moment estimates from a small sample of data tend to be highly biased and uncertain. The difficulty in obtaining accurate moment estimates from small samples has been the main impediment to the application of the MaxEnt Principle in extreme quantile estimation. The present paper is an attempt to overcome this problem by the use of probability-weighted moments (PWMs), which are essentially the expectations of order statistics. In contrast with ordinary statistical moments, higher order PWMs can be accurately estimated from small samples. By interpreting the PWM as the moment of quantile function, the paper derives an analytical form of quantile function using MaxEnt principle. Monte Carlo simulations are performed to assess the accuracy of MaxEnt quantile estimates computed from small samples.     

Optimal planning of distributed generation from the DisCo and the DGO viewpoints considering the uncertainties in future demand and electricity price

Since electricity demand is increasing continuously, it is necessary to invest in expansion of distribution network capacity. From the asset management point of view, it is necessary to encourage the private sector to invest in distribution network. To do this, distribution network managers must provide important opportunities for private sector to profit from their investments. One of the options for private sector is to invest on distributed generations (DGs). In this regard, distribution company (DisCo) must sign power purchase agreement (PPA) with DG owners (DGOs). So, optimal siting, sizing and PPA rates from economic point of view are important challenges which are considered as the main contribution of this paper. The proposed methodology of this paper applies load and price uncertainties into the planning problem. The proposed scheme involves using non-dominated sorting genetic algorithms II (NSGA II), since it attains non-dominated solutions in which DisCo and the DGO can put their personal preferences into practice. To evaluate the effectiveness of the suggested method, the computer simulations are done on a 33-bus distribution network and the results are discussed.     

A multi-objective framework for energy resource scheduling in active distribution networks

Purpose: The purpose of this paper is to investigate the impacts of electric vehicles' (EVs) charging/discharging decisions in energy resources scheduling problem of active distribution networks. Design/methodology/approach: The problem under study is modelled as a two-stage optimisation problem in which the main requirements of EV owners are introduced as an objective function of the first stage. The total energy costs and the emission factor are considered as the main criteria of the second stage. The output generation schedules of distributed generation (DG) technologies together with the charging/discharging schedule of EVs are proposed as decision variables of the energy scheduling problem. Therefore, some effective methods are presented to model the uncertainties associated with these variables. Findings: The results proved that an efficient compromise can be reached between the emission factor and the energy cost of the system. In addition, it has been emphasised on the importance of such comprehensive energy scheduling frameworks. Originality/value: This paper contributes by: (a) providing a multi-objective framework for energy scheduling of active distribution networks, (b) extracting the mathematical model of this two-stage problem and (c) employing a linearised optimisation model to reach its global optimal solution.     

SQMZ系列双曲面支座有限元分析

为了确切掌握SQMZ系列双曲面支座在运营状态下的应力分布状态,应用ANSYS程序对该系列支座进行了空间应力分析,计算结果表明,运营阶段支座各部位应力均满足设计要求,支座设计是安全、可靠的。     

The basic properties of some typical systems’ reliability in interval form

A proper mathematical representation of uncertainties is indispensable for reliability analysis of a practical engineering structural system. A general uncertainty analysis approach is probability bounds analysis (PBA), which propagates constraints on a distribution function through mathematical operations. The uncertainty about a probability distribution is represented by the set of cumulative distribution functions lying entirely within a pair of bounding distribution functions, which is called a P-box. Interval analysis as a special case of PBA is useful when there is no or less probabilistic information. It is common sense that great efforts must be paid to get enough probabilistic information used for probabilistic analysis of large and complex engineering structural systems. Even if there is no or less probabilistic information; the interval of possible values of probability of an event can be easily specified, such as the interval value of each element’s reliability of an engineering structural system.

This paper aims to introduce the concept of system reliability and its relationship to the reliability of its individual elements in an interval form. In terms of extension principle, interval arithmetic and possibility degree formula (PDF) for ranking interval numbers, basic properties of system reliability in interval form are investigated. The conclusion is that relationships between point reliability (point reliability used to describe a precise value of probability reliability is distinct with interval reliability) of some typical systems, such as series system, parallel system, series–parallel system, parallel–series system and r/n(G) system, etc., and point reliability of their individual elements are maintained in their interval forms. This is called quasi-consistency in this paper. A simple review of order relations of interval numbers, which will play an important role in interval reliability analysis, is given. The proposed quasi-consistency establishes the foundations for interval reliability analysis of a complex engineering structural system.     

A study on environmental impact of nanofluid-based concentrating solar water heating system

The rising cost of fossil fuels and the growing concern for a clean environment have paved the way for adoption of renewable and sustainable sources of energy. Domestic water heating can result from solar energy, a clean and renewable alternative, which can at the same time help to reduce greenhouse gas emissions. This paper examines the potential of the nanofluid-based concentrating solar water heating system (NCSWHS) as an alternative to systems based on fossil fuels. The paper reports a quantitative assessment to assess the potential environmental benefits which could be obtained from NCSWHS if substituted for those using fossil fuels. The analysis reveals that considerable emission reductions (about 2.2 x 10³ kg of CO₂/household/ year) and fuel savings can be achieved if the NCSWHS are adopted.     

Applied Energy

It is acknowledged that the conventional design methods can easily lead to oversized system or unsatisfactory performance for different design conditions. Most existing studies on design optimization of net zero energy building ( nZEB) are conducted based on deterministic data/information. However, the question is:How is the actual performance of a design nZEB in different years considering uncertainties? This study, therefore, proposed a robust design method for sizing renewable energy systems in nZEB concerning uncertainties in renewable resources and demand load. The proposed robust design method is applied to the planning of renewable energy system for the Hong Kong Zero Carbon Building. The annual performance of nZEB under the optimal design options are systematically investigated and compared using the proposed robust design method and the deterministic method. It is meaningful to obtain a fitting formula to identify the relationship between the probability of achieving annual zero energy balance and the design mismatch ratio. On the basis of Monte Carlo uncertainty propagation methods, the uncertainty of nZEB performance is quantified which provides flexibility for designers in selecting appropriate design options according to the required probability of achieving nZEB during the design stage.     

The politics of renewable energy: a perspective from the UK

Until recently, Renewable Energy Technologies were seen as of marginal importance. However, with the use of fossil fuels likely to be increasingly constrained in the future, and nuclear power facing continuing uncertainties, the renewables have begun to look more attractive. In which case we are likely to see a whole new set of technical, strategic and political issues enter the energy policy debate. This paper explores some of the new issues that are likely to emerge as renewable energy challenges existing technologies and energy policy paradigms.     

Optimizing the design of a solar cooling system using central composite design techniques

This paper presents the development of a method to optimize a solar-assisted cooling system with limited budget constraints. Regression analysis is used to identify the relationship between the solar fraction and the system factors according to the data provided by experiments. In order to obtain an accurate model to estimate the problem using small number of experimental trials, the method of central composite design (CCD) from design of experiment (DE) is used as a key technique. The experimental trials are conducted in the transient energy system simulation (TRNSYS) tool. Finally, the optimization problem is formulated and solved by including the model as the objective function, the physical constraints of the system factors, and the budget limit. A case study was conducted to apply this optimization method to the design of a solar-assisted double-effect absorption cooling system installed in a small-sized office building in West Lafayette, IN, USA. The results show the developed optimal model strongly agrees with the physical system model in TRNSYS. This optimization method can be generally applied to different types of solar cooling systems, and other renewable energy systems.     

Redundancy-based service life assessment of corroded reinforced concrete elements considering parameter uncertainties

In order to assess the structural reliability and redundancy with respect to deterioration, appropriate models have to be selected which adequately describe the deterioration process. The parameters associated with these models have to be estimated based on statistical inference. In general, the uncertainties that arise from the estimation of parameters are not accounted for in reliability assessment and the obtained structural reliability indices are assumed to be constant values. When parameter uncertainties are considered, the structural reliability index can, however, be considered as a random variable which inevitably influences the full-probabilistic decision-making process. Furthermore, the structural reliability indices which are used in a reliability-based redundancy factor can be considered as random variables. Hence, this redundancy factor itself is a random variable as well. In this paper, a full-probabilistic framework is developed which allows for the service life assessment of reinforced concrete elements subjected to corrosion based on a probability-based redundancy factor and taking into account parameter uncertainties. It is proven that these parameter uncertainties have a significant influence on the lifetime estimates of concrete elements subjected to corrosion. Finally, a simplified method is proposed which allows to incorporate parameter uncertainties in the redundancy assessment.