A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Study on Long-Term Generation Expansion Planning upon the LNG Price Fluctuations

About 37% of South Korea’s greenhouse gas emission is from electricity generation. Most of the country’s electric power is fundamentally generated by nuclear, thermal and LNG facilities. And LNG, of them, is characterized to require high cost for power generation but CO2 coefficient is lower than thermal generation. Amid the ongoing global efforts to tackle global warming, shale gas introduction and changing global environment, LNG prices are expected to fluctuate. Against this backdrop, this paper seeks to perform scenario tests on LNG fuel cost fluctuation and examine its long-term effects on generation expansion planning.

Research on the Impact of Aging and New Generation in the Popula­tion Structure on China’s Real Estate Price Volatility

To clarify the internal mechanism of the influence of the aging population and the new generation on housing prices is helpful to scientifically analyze and predict the trend of housing prices and the aging population and the new generation.This paper uses the intergenerational overlap model of the two periods as the theoretical basis,and uses the provincial panel data from 1998 to 2018 to study the impact of the elderly population and the new generation on the price fluctuations of commercial housing.The results of the study show that on the whole,both the aging population and the new generation have promoted the rise in commodity housing prices.However,the regional heterogeneity is significant.The aging population has the most significant impact on housing price increases in developed and general developed areas,and has no significant impact on housing price increases in other places.The new generation has a negative impact on housing prices in backward areas and a positive impact on housing prices in other areas.Looking further,using the ARIMA model to predict housing prices in the next 10 years,it is concluded that housing prices will show a slow upward trend in the next 10 years.Therefore,the government can ensure the stable development of the real estate market by revitalizing the second-hand housing market and implementing housing projects.

Quantum particle swarm optimization for micro-grid system with consideration of consumer satisfaction and benefit of generation side

Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery.

Analysis of Nigerian Electricity Generation Multi Year Tariff Order Pricing Model

The Multi Year Tariff Order (MYTO) is the Nigerian Electricity Regulatory Commission (NERC) pricing framework for determining the Nigerian Electricity Supply Industry (NESI) pricing model. One of the objectives of the NERC’s MYTO pricing model is to ensure regulated electricity end user tariff without compromising return on investment. Achieving this objective is imperative to attract investors in the growing Nigerian electricity market. However, NESI has hitherto been faced with challenges ranging from its inability to provide sufficient power to its customers to not being viable enough to provide return on capital invested. In this paper, sensitivity analysis of power plant operation and performance parameters on the cost of electricity (CoE) generation using MYTO (power generation) pricing model were evaluated. Thermodynamic modeling and simulation of an open cycle gas turbine (OCGT) was carried out to augment scarce data on power plant performance and operation in Nigeria. Sensitivity analysis was carried out using probabilistic method based on Monte Carlo simulation (MCS) implemented in commercial software (@ Risk®). The result highlighted sensitivity of the model input parameters to cost of electricity generation based on technical and financial assumptions of MYTO model. Seven most influential parameters affecting generation cost were identified. These parameters and their correlation coefficients are given as: 1) foreign exchange rate, 0.76;2) cost of fuel, 0.51;3) thermal efficiency, -0.23;4) variable operation and maintenance cost, 0.22;5) fixed operating and maintenance cost, -0.03;6) capacity factor, -0.02;and 7) average capacity degradation, 0.01. Based on the gas turbine engine and input parameter distributions statistics for this study, the generation cost lies between 9.84 to 15.45 N/kWh and the probabilities of CoE within these values were established.

Optimal Time-of-use Pricing for Renewable Energy-powered Microgrids: A Multi-agent Evolutionary Game Theory-based Approach

While price schedules can help improve the economic efficiency of renewable energy-powered microgrids,timeof-use(TOU)pricing has been identified as an effective way for microgrid development,which is presently limited by its high costs.In this study,we propose an evolutionary game theoretic model to explore optimal TOU pricing for development of renewable energy-powered microgrids by applying a multi-agent system,that comprises a government agent,local utility company agent,and different types of consumer agents.In the proposed model,we design objective functions for the company and the consumers and obtain a Nash equilibrium using backward induction.Two pricing strategies,namely,the TOU seasonal pricing and TOU monthly pricing,are evaluated and compared with traditional fixed pricing.The numerical results demonstrate that TOU schedules have significant potential for development of renewable energy-powered microgrids and are recommended for an electric company to replace traditional fixed pricing.Additionally,TOU monthly pricing is more suitable than TOU seasonal pricing for microgrid development.

Research on Data Theory of Value

This paper explores the data theory of value along the line of reasoning epochal characteristics of data-theoretical innovation-paradigmatic transformation and,through a comparison of hard and soft factors and observation of data peculiar features,it draws the conclusion that data have the epochal characteristics of non-competitiveness and non-exclusivity,decreasing marginal cost and increasing marginal return,non-physical and intangible form,and non-finiteness and non-scarcity.It is the epochal characteristics of data that undermine the traditional theory of value and innovate the“production-exchange”theory,including data value generation,data value realization,data value rights determination and data value pricing.From the perspective of data value generation,the levels of data quality,processing,use and connectivity,data application scenarios and data openness will influence data value.From the perspective of data value realization,data,as independent factors of production,show value creation effect,create a value multiplier effect by empowering other factors of production,and substitute other factors of production to create a zero-price effect.From the perspective of data value rights determination,based on the theory of property,the tragedy of the private outweighs the comedy of the private with respect to data,and based on the theory of sharing economy,the comedy of the commons outweighs the tragedy of the commons with respect to data.From the perspective of data pricing,standardized data products can be priced according to the physical product attributes,and non-standardized data products can be priced according to the virtual product attributes.Based on the epochal characteristics of data and theoretical innovation,the“production-exchange”paradigm has undergone a transformation from“using tangible factors to produce tangible products and exchanging tangible products for tangible products”to“using intangible factors to produce tangible products and exchanging intangible products for tangible products”and ultimately to“using intangible factors to produce intangible products and exchanging intangible products for intangible products”.

Battery electric buses charging schedule optimization considering time-of-use electricity price

Purpose–This paper aims to optimize the charging schedule for battery electric buses(BEBs)to minimize the charging cost considering the time-ofuse electricity price.Design/methodology/approach–The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model.The objective is to minimize the total charging cost of the BEB fleet.The charge decision of each BEB at the end of each trip is to be determined.Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.Findings–This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line.The results show that the total charge cost with the optimized charging schedule is 15.56%lower than the actual total charge cost under given conditions.The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent,which can provide a reference for planning the number of charging piles.Originality/value–Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

Short-term Scheduling of Steam Power System in Iron and Steel Industry under Time-of-use Power Price

A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cost,equipment maintenance cost and the charge of exchange power with main grid.The model took into account the varying nature of surplus byproduct gas flows,several practical technical constraints and the impact of TOU power price.All major types of utility equipments,involving boilers,steam turbines,combined heat and power（CHP）units,and waste heat and energy recovery generators（WHERG）,were separately modeled using thermodynamic balance equations and regression method.In order to solve this complex nonlinear optimization model,a new improved particle swarm optimization（IPSO）algorithm was proposed by incorporating time-variant parameters,a selfadaptive mutation scheme and efficient constraint handling strategies.Finally,a case study for a real industrial example was used for illustrating the model and validating the effectiveness of the proposed approach.

Economic Optimization Dispatching Strategy of Microgrid for Promoting Photoelectric Consumption Considering Cogeneration and Demand Response

A system combining photovoltaic power generation and cogeneration is proposed to improve the photoelectric absorption capacity. First, a time-of-use price strategy is adopted to guide users to change their electricity consumption habits for participation in the demand response, and a demand response model is established. Then, particle swarm optimization(PSO)is used with the aim of minimizing the operation cost of the microgrid to achieve economic dispatching of the microgrid. This considers power balance equation constraints, unit operation constraints, energy storage constraints, and heat storage constraints. Finally, the simulation results show the improved level of photoelectric consumption using the proposed scheme and the economic benefits of the microgrid.

Time-of-use Pricing Model Considering Wind Power Uncertainty

Large-scale new energy pressures on the grids bring challenges to power system's security and stability.In order to optimize the user's electricity consumption behavior and ease pressure,which is caused by new energy on the grid,this paper proposes a time-of-use price model that takes wind power uncertainty into account.First,the interval prediction method is used to predict wind power.Then typical wind power scenes are selected by random sampling and bisecting the K-means algorithm.On this basis,integer programming is used to divide the peak-valley period of the multi-scenes load.Finally,under the condition of many factors such as user response based on consumer psychology,user electricity charge and power consumption,this paper takes the peak-valley difference of equivalent net load and the user dissatisfaction degree as the goal,and using the NSGA-II multi-objective optimization algorithm,evaluates the Pareto solution set to obtain the optimal solution.In order to test the validity of the model proposed in this paper,we apply it to an industrial user and wind farms in Yan'an city,China.The results show that the model can effectively ensure the user's electrical comfort while achieving the role of peak shaving and valley flling.

Current State and Prospect of China Natural Gas Industry

Natural gas output remained stable growth and reached 130.9 billion cubic meters in 2015, 3% higher than the same period last year. Shale gas saw huge progress. China titus became the third country in the world fu!filling commercial development after U.S. attd Canada. Natural gas import growth and growth rate declined obviously, and the imported pipeline gas and LNG totaled 61.2 billion cubic meters in 2015. Apparent natural gas consumption was 186.5 billion cubic meters in 2015, rising by 4.4% as compared with the same period last year, but it hit a historic low. There is higher dozonward pressure on domestic macro economy in 2016. However, natural gas demand will see more rapid growth, propelled by such favorable factors as gas price regulation and environmental protection policies. It is prospected that natural gas market will take a turn for the better than in 2015, and natural gas supply will still be rich in general in 2016.

Hybrid Scenario Generation Method for Stochastic Virtual Bidding in Electricity Market

Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using scenarios.This paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average(SARIMA)model and historical data.The electricity price spikes are first identified by using an outlier detection method.Then,the historical data are decomposed into base and spike components.Next,the base and spike component scenarios are generated by using the SARIMA-and historical data-based methods,respectively.Finally,the electricity price scenarios are obtained by combining the base and spike component scenarios.Case studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed method.The optimal bidding strategies of the virtual bidder are generated by solving a stochastic optimization problem using the electricity price scenarios generated by the proposed method,the SARIMA method,and a historical data-based method,respectively.Case study results show that the proposed method is better than the SARIMA method in preserving statistical properties of the electricity price in the generated scenarios and is better than the historical data-based method in predicting the future trend of the electricity price and,therefore,can help the virtual bidder earn more profit in the electricity market.

Power Optimization Strategy Considering Electric Vehicle in Home Energy Management System

With the development of smart grid, residents have the opportunity to schedule their household appliances （HA） for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this paper, we introduce the structure of home energy management system （EMS） and then propose a power optimization strategy based on household load model and electric vehicle （EV） model for home power usage. In this strategy, the electric vehicles are charged when the price is low, and otherwise, are discharged. By adopting this combined system model under the time-of-use electricity price （TOUP）, the proposed scheduling strategy would effectively minimize the electricity cost and reduce the pressure of the smart grid at the same time. Finally, simulation experiments are carried out to show the feasibility of the proposed strategy. The results show that crossover genetic particle swarm optimization algorithm has better convergence properties than traditional particle swarm algorithm and better adaptability than genetic algorithm.

Cost implications of increased solar penetration and time-of-use rate interactions

Electricity-grid operators are facing new challenges in matching load and generation due to increased solar generation and peak-load growth.This paper demonstrates that time-of-use(TOU)rates are an effective method to address these challenges.TOU rates use price differences to incentivize conserving electricity during peak hours and encouraging use during off-peak hours.This strategy is being used across the USA,including in Arizona,California and Hawaii.This analysis used the production-cost model PLEXOS with an hourly resolution to explore how production costs,locational marginal prices and dispatch stacks(type of generation used to meet load)change due to changes in load shapes prompted by TOU rates and with additional solar generation.The modelling focused on implementing TOU rates at three different adoption(response)levels with and without additional solar generation in the Arizona balancing areas within a PLEXOS model.In most cases analysed,implementing TOU rates in Arizona reduced reserve shortages in the Western Interconnect and,in some cases,very substantially.This result is representative of the interactions that happen interconnection-wide,demonstrating the advantage of modelling the entire interconnection.Production costs were decreased by the additional solar generation and the load change from TOU rates,and high response levels reduced the production costs the most for high-solar-generation cases.Load change from TOU rates decreased locational marginal prices for a typical summer day but had inconsistent results on a high-load day.Additional solar generation decreased the usage of combustion turbines,combined cycles and coal-fired generation.

APRICING METHOD of SURPLUS THERMAL POWER EXCHANGE BETWEEN GRIDS

This paper presents a case study on surplus thermal power being exchange between power grids and associate prices. The analysis shows the potential value of surplus thermal power exchange between grids under the condition whatever a grid is excessive or short in capacity.

Current Situation and Prospects of the Development of China’s Natural Gas Industry

In 2018,China's natural gas market reached a new level of development,with apparent consumption of 280.3 billion m3,up by 18.1%over the same period in the previous year.Domestic production grew steadily,reaching 157 billion m3,up by 7.2%over the same period in the previous year.Natural gas imports grew rapidly,with imports of pipeline gas and LNG totalling 124.2 billion m3.In terms of trade types,imports of LNG continue to exceed those of pipeline gas.In 2019,there has been downward pressure on the macro economy,and the development of the main gas sector has slowed down.Driven by environmental protection policies,the natural gas market continues to maintain rapid growth.However,it is difficult for the levels of increment and growth to reach those of the previous two years,and the growth rate of market demand is predicted to reach 10.7%.

Electricity Pricing for Natural Gas CHP Projects The Higher, the Better?

Regarding the state's policy that gives a higher on-grid electricity price to natural gas CHP (combined heat and power) projects, this paper studies the effect of it on the operation of those projects by theoretical analysis and a case study. It concludes that on-grid electricity price on the high side, compared to heat price, will lead power plants to produce more electricity but less heat, thus causing decrease of the plants' thermal eff iciency and harm to energy saving of the whole society.

Power ＂Smart Pricing＂ with and without Smart Meters

Over the past several years, the Taiwan Power Company has launched two smart pricing programs to assess the demand response of residential customers： the TOU （time-of-use） rate scheme and the DRI （demand reduction incentive） scheme. This paper discusses these two programs and evaluates their respective performances. We develop an efficient approach based on marginal cost pricing to redesign the TOU rate scheme. In our finding, the TOU price levels could be revised to encourage more customers to participate by enlarging the price gap. Moreover, the DRI scheme can be further improved in order to reach an efficient win-win solution among customers, the utility and society. This can be achieved via a careful design of incentive tariff discounts to take account of the time-of-use or location-specific features of the power supply/demand condition.