Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Spatio-temporal Evolution of China’s Economic Power Based on Asymmetric Theory

In the 21 st century, economic interdependence between countries has gradually become an important source of state power. Globalization and the rise of China’s economy have had a significant impact on other economies around the world. China’s economic influence is becoming more and more important. On the basis of the sensitivity dependence and vulnerability dependence of asymmetric interdependence theory, the economic spillover between countries is selected to construct a quantitative model to measure economic power. This paper analyzes the evolution of China’s economic power both in time and space from 2000 to 2014. Four conclusions were drawn from the study: 1) The spatial spillover effect of China’s economy on other countries has been continuously growing since the beginning of the 21 st century, which has brought about the rapid increase in China’s economic power;2) China’s economic power has been significantly strengthened both in intensity and scope, having expanded from the surrounding countries to the whole world. In 2014, China had a high amount of economic power over 18 countries from the 41 main countries in the world, compared to 2 in 2000;3) China’s power in terms of sensitivity dependence takes on an evolutionary trend from point distribution to flaky growth, and it shows a process ‘from point to line’ from the aspect of spatial diffusion. Furthermore, China’s power in terms of vulnerability dependence shows a trend that extends from the surrounding countries to European and South American countries;4) comprehensively, China’s power in terms of vulnerability dependence is higher than that of sensitivity dependence, and the spatial distribution pattern demonstrates a process from centralization to decentralization from 2000 to 2014.

Measurement of Economic Power： An Engineering Approach

Business Directory defines economic power as ＂conditions of having sufficient productive resources at command that give the capacity to make and enforce economic decisions, such as allocation of resources and apportioning of goods and services＂. This qualitative definition may be sufficient for general discussions, but it does not offer any quantitative measurements for management and control. Is there a way to measure economic power for analysis and synthesis of economic systems in this 21st century？ This paper answers this question by introducing a set of quantitative terms from the field of electrical engineering, used in power measurement and control of electrical systems. Circuit theory concepts are introduced to show how the relationship between cash and cash flow in finance is analogous to that of electric charge and electric current and how education level, as economic potential, is analogous to electrical potential that causes current to flow in a circuit. An individual is identified as the basic cash flow source in the economy. Circuit models for an individual as well as a typical production facility are developed and demonstrated with an example. Equations for the measurement of economic power, losses, efficiency, and power factor are presented. It is shown that the individuals are the basic cash flow sources and producers of economic power.

Simulation Analysis for Massive Deployment of Variable Renewables Employing an Optimal Power Generation Mix Model

This paper develops a high time-resolution optimal power generation mix model in its time resolution of 10 minutes on 365 days by linear programming technique. The model allows us to analyse the massive deployment of photovoltaic system and wind power generation in power system explicitly considering those short-term output variation. PV （photovoltaic） and wind output are estimated, employing meteorological database. Simulation results reveal that variable fluctuation derived from a high penetration level of those renewables is controlled by quick load following operation of natural gas combined cycle power plant, pumped-storage hydro power, stationary NAS （sodium and sulfur） battery and the output suppression control of PV and wind. It additionally turns out that the operational configuration of those technologies for the renewable variability differs significantly depending on those renewable output variations in each season and solving the seasonal electricity imbalance as well as the daily imbalance is important if variable renewables are massively deployed.

The Soft Power of India Viewed from Its Economic Reform

India's economic reform started from the realm of finance, which is within the category of soft power of a country. The wax and wane of the overall power of a country is largely determined by the interplay of hard and soft pwers in question. India's soft power has its own characteristics and advantages, which chiefly manifest in its culture, parliamentary democracy, education and finance. In the 21st century, these advantages will continue to have significant play.

Distributed event-triggered optimal power management of distribution networks considering dynamic tariff

This paper introduces a novel fully distributed economic power dispatch(EPD)strategy for distribution networks,integrating dynamic tariffs.A two-layer model is proposed:the first layer comprises the physical power distribution network,including photovoltaic(PV)sources,wind turbine(WT)generators,energy storage systems(ESS),flexible loads(FLs),and other inflexible loads.The upper layer consists of agents dedicated to communication,calculation,and control tasks.Unlike previous EPD strategies,this approach incorporates dynamic tariffs derived from voltage constraints to ensure compliance with nodal voltage constraints.Addi-tionally,a fast distributed optimization algorithm with an event-triggered communication protocol has been developed to address the EPD problem effectively.Through mathematical and simulation analyses,the proposed algorithm's efficiency and rapid conver-gence capability are demonstrated.

The Lanczos-Chebyshev Pseudospectral Method for Solution of Differential Equations

In this paper, we propose to replace the Chebyshev series used in pseudospectral methods with the equivalent Chebyshev economized power series that can be evaluated more rapidly. We keep the rest of the implementation the same as the spectral method so that there is no new mathematical principle involved. We show by numerical examples that the new approach works well and there is indeed no significant loss of solution accuracy. The advantages of using power series also include simplicity in its formulation and implementation such that it could be used for complex systems. We investigate the important issue of collocation point selection. Our numerical results indicate that there is a clear accuracy advantage of using collocation points corresponding to roots of the Chebyshev polynomial.

Development and application of plant transformation techniques

Genetic transformation is a powerful biotechnology for introducing novel genes into economically important plants from distantly-related plants or even unrelated species such as microbes and animals.This feat is impossible to be achieved by conventional breeding techniques.Development of transgenic plants has been a controversial subject since 1971 when the first genetically modified organism（GMO）was developed（James and Krattiger1996）.

Energy-Efficiency Economics as a Resource for Energy Planning

The objective of this work is a multi-criteria decision-making assessment that aims to facilitate the Energy-Efficiency Economics, introducing the Analytic Hierarchy Process (AHP) as part of power-system planning tool for an energy-efficiency application. It addresses to include qualitative aspects in the decision-making agendas of energy-efficiency projects. The manuscript details the limitations of non-rigorous financial analysis and proposes an alternative for including energy-efficiency measures in discussions pertaining to the financial opportunities available to any investor, and it presents the methodology that supports the qualitative aspects and the software package used to execute this methodology. As case study a complete example including a sensitivity analysis is presented.

Influence of Taxation on Supply and Demand in Tomorrow's Crowd Energy Paradigm

The emergence of the energy self-sufficient home presents a new role for government taxation. Policymakers now face the challenge of reflecting this technological change in their decision-making and must assume a greater level of engagement. This paper proposes a number of original fiscal concepts for policymakers to implement in the support of micro-grid development. These are designed to optimise a sustainable transition away from the centralised energy system whilst creating shared value among stakeholders throughout the value chain. Concepts are based on residential micro-grid schemata in Switzerland and are applicable in other countries.

Power-to-gas technology in energy systems:current status and prospects of potential operation strategies

Regarded as a long-term, large capacity energy storage solution, commercialized power-to-gas(PtG) technology has attracted much research attention in recent years.PtG plants and natural gas-fired power plants can form a close loop between an electric power system and a natural gas network. An interconnected multi-energy system is believed to be a solution to the future efficient and environmental friendly energy systems. However, some crucial issues require in-depth analysis before PtG plants can be economically implemented. This paper discusses current development status and potential application of PtG plants in the future interconnected multi-energy systems, and further analyzes the costs and benefits of PtG plants in different application scenarios. In general, the PtG plants are not economical efficient based on current technologies and costs. But the situation is likely to change with the development of PtG technologies and interconnected operation of gas-electricity energy system.

The economics of soft power:Reliance on economic resources and instrumentality in economic gains

Soft power is commonly presented as an alternative or a complement to harder forms of power,such as military and economic might.However,while it is safer to say that soft power does not depend on military capabilities,it is not as straightforward to separate soft power from its economic counterpart.Juxtaposing various soft power rankings with a country’s economic assets may reveal how soft power relies on economic resources.Moreover,when closely scrutinised,it is possible to appreciate how dynamics of attraction are also closely intertwined with economic gains.In an increasingly connected world,soft power could prove to be instrumental in achieving economic success.Economic effects are expected to be more consistent,observable,and attainable when compared to political ones,such as spreading democracy.Far from arguing that soft power is unable to assist in achieving political goals in the long term,this paper aims instead to highlight how a narrow culture-centred analysis of soft power greatly limits the understanding of this power in the real world,and results in the underestimation of its value and impact.China’s quest to connect the world through its Belt and Road Initiative(BRI)is a topical case through which to further explore the soft–economic power nexus.

Comparing the economic value of lithium-ion battery technologies in the nine wholesale electricity markets in North America

Lithium-ion batteries are becoming critical flexibility assets in future electric power systems.Batteries can arbitrage price differences in wholesale electricity markets to make a profit while at the same time reducing total system operating costs and improving renewable energy integration.However,lithium-ion batteries have a limited lifetime due to capacity degradation,and one battery pack can only make a limited profit before reaching its end-of-life.In this paper,we screen the profit potential of Lithium iron phosphate(LFP),nickel manganese cobalt(NMC),and lithium nickel cobalt aluminum oxides(NCA)batteries in all nine wholesale electricity markets in North America.We apply a systematic dynamic valuation framework that finds the highest revenue potential for the con-sidered lithium-ion battery project subjecting to its degradation mechanism,while the degradation model used in the valuation is derived based on real lab test data over varying cycle conditions.The study found that battery valuation depends largely on battery technology and storage duration and varies across operational locations.Moreover,the study revealed that calendar life has a greater impact on battery valuation than cycle life for an 8-years calendar life scenario while cycle life shows greater impact for a 15-year calendar life scenario for all battery technologies.This impact is more pronounced in LFP than in NMC and NCA.The study recommends battery operators consider strategies that would maximize a longer cycle life or calendar life usage of a battery as this would accumulate higher profits over its lifetime.

Optimal Coordinated Bidding Strategy of Wind and Solar System with Energy Storage in Day-ahead Market

Although wind and solar power is the major reliable renewable energy sources used in power grids,the fluctuation and unpredictability of these renewable energy sources require the use of ancillary services,thereby increasing the integration cost.This study proposes a wind,solar,and pumped-storage cooperative(WSPC)model that can be applied to large-scale systems connected to dispersed renewable energy sources.This model provides an optimized coordinated bidding strategy in the day-ahead market,along with a method to facilitate revenue distribution among participating members.This model takes advantage of the natural complementary characteristics of wind and solar power while using pumped storage to adjust the total output power.In the coordinated bidding strategy,a proportion of the energies is provided as firm power,which can lower the ancillary service requirement.Moreover,a multi-period firm power-providing mode is adopted to reflect the wind-solar output characteristics of each period accurately.The duration of each period is selected as a variable to accommodate seasonal characteristics.This ensures that the provision of firm power can maintain a high proportion under varied connected ratios of wind-solar,thereby obtaining higher revenue.By using the revenue distribution method,the short-term influencing factors of the cooperative model are considered to provide the economic characteristics of wind farms and photovoltaic stations.In this way,revenue distribution can be fairly realized among the participating members.Finally,the effectiveness and economy of the proposed model are validated based on actual data obtained from the power grid in California,USA.

A new technique for solving the multi-objective optimization problem using hybrid approach

Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO2 emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a hi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach （firefly algorithm-real genetic algorithm, FFA-RGA） has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi- objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.

Fair and unfair inequality in China

The paper questions the normative value in the Chinese case of standard measures of aggregate income inequality such as the Gini coefficient.Evidence is adduced that people have narrow frames of reference and that they distinguish between income inequalities that they perceive to be fair and those that they perceive to be unfair.It is suggested that value judgements about what is fair or unfair can be guided by people’s own perceptions.People’s perceptions of unfairness can also be important to a government concerned to avoid social instability.The estimation of happiness functions can help to make the relevant distinctions.Examples are given of how fair and unfair inequalities might be identified,using either a criterion of people’s perceptions or one of informed judgements.Unfairness might be more strongly felt in inequalities of economic power than in inequalities of income,although the former can in turn result in inequalities of income.An argument is made for China researchers to extend inequality research and research instruments towards an economics of fairness and unfairness.

Preface

Complex networks are ubiquitous in our lives. Representative examples are the Internet, social networks, biological networks, E-commerce networks, electrical power grids, and larger-scale engineering systems. It is well known that the Internet has been a powerful engine for our societal evolution and technological innovation. Nowadays, network science and engineering faces fundamental challenges, such as understanding the complexity of various large-scale networks, developing new architectures and exploiting new substrates, and enabling new applications and new economics. To a better future, the complex networks in our lives will need to be better： more accessible, more reliable, more predictable, and more secure.