Power Price Research Articles

Has the Wind Power Price Policy Promoted the High-Quality Development of China’s Wind Power Industry?—Analysis Based on Total Factor Productivity

The wind power price policy has promoted the rapid development of the wind power industry in China. However, China’s wind power industry is facing high-quality development problems such as wind curtailment and blind investment. Exploring the relationship between the change in wind power price policy and China’s high-quality development of wind power is of great significance for the energy system to achieve carbon neutrality. This paper constructs an SBM-GML global covariance model, calculates the total factor productivity of wind power in 30 provinces in China from 2015 to 2019 and conducts index decomposition, and selects provincial panel data from 2015 to 2019 to empirically test the impact of the wind power price policy on the total factor productivity of wind power in China. The results show that the wind power price policy can significantly improve the total factor productivity of wind power, and the results of the endogenous test and robustness test show the effectiveness of the model. The wind power price policy is helpful to promote the technological progress of wind power, affect the reduction of the price difference between wind power benchmark price and coal power benchmark price, and then promote the improvement of wind power total factor productivity so as to meet the policy requirements of wind power full grid parity. In addition, the impact of wind power price policy on the total factor productivity of wind power has obvious regional heterogeneity. Future price policy formulation should clarify industry development expectations, consider regional differences, and establish a sound, market-oriented electricity pricing mechanism.

Development of a Joint Penalty Signal for Building Energy Flexibility in Operation with Power Grids: Analysis and Case Study

Electricity generation from renewable energy reduces greenhouse gas emissions and, in the long term, the cost of electricity in power grids. However, there is currently no strong positive correlation between greenhouse gas intensity and electricity spot prices in Germany, despite increasing renewable energy penetration. Therefore, energy flexibility programs that rely on demand response may not be fully effective in reducing carbon emissions unless the energy market aligns consistently with carbon emission factors. To address this issue, we propose a model for joint signals consisting of power grid climate gas intensity and price signals that can achieve both environmental and economic benefits for building energy flexibility applications. Next, to assess the maximum possible flexibility hours from the grid side, we explore penalty signal threshold limits with daily and biweekly aggregation. Using a case study, we analyze energy flexibility with joint signals to explore their effect on greenhouse gas emissions and building operation cost. Our results suggest that joint signals can be more effective than a single type of signal in promoting energy flexibility.

Natural Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost and Attention–VECM Model

In recent years, frequent linkage events of natural gas and electricity prices have seriously affected the stability of the energy supply market. In view of the increasingly close linkage relationship between gas and electricity prices, a gas–electricity price linkage analysis method considering the benefit–cost and price transmission of gas units is proposed. Firstly, a benefit–cost model of gas units based on risk premium theory is constructed to reflect the willingness of gas units to offer prices in different market environments. Secondly, a vector error correction model (VECM)-based gas–electricity price conduction analysis method was proposed to study the long-term co-integration relationship between gas–electricity prices and the short-term fluctuation conduction process. Then, aiming at the problem of delay in price transmission, a time-delay analysis method of gas–electricity price based on the attention mechanism is proposed. Finally, the gas price index and average gas power price data of China from August 2020 to November 2022 are used to verify the validity of the proposed model; the numerical results show that the proposed method can determine the long-term stable fitting relationship between gas and electricity prices, and can also analyze and judge the conduction direction of gas and electricity price fluctuations in different time periods.

Evaluation and ranking of battery electric vehicles by Shannon’s entropy and TOPSIS methods

In today’s culture, everyone requires transportation. Polluting vehicles like those that run on gasoline or diesel are being swiftly replaced with all-electric cars. Fully electric vehicles are much more environmentally beneficial because they emit no exhaust. The market for electric vehicles has expanded quickly in recent years, making it difficult for consumers to evaluate them and a major source of concern. Our primary responsibility in this study is to rank the fifteen different brands and types of electric automobiles that are now available for purchase globally based on the established criteria. This study evaluates fifteen different electric vehicles, according to their different ten criteria such as full charge time, purchasing price, quick charge time, maximum power, electric range, battery capacity, top speed, cargo volume, acceleration, and unladen weight. For the assessment in this study, the electric vehicle dataset was obtained from automobile manufacturers. For the evaluation of electric vehicles, we use two methods. The first method, Shannon’s entropy, is used to determine the electric car’s criterion weight, while the second method, TOPSIS, is used to rank electric vehicles. The study’s findings indicate that the quick charge time, maximum power, electric range, battery capacity, top speed, and base price aspects of electric cars gained popularity. An evaluation of electric cars serves as the foundation for the research’s validity.

Green power pricing and matching efficiency optimization for peer-to-peer trading platforms considering heterogeneity of supply and demand sides

Green power pricing and matching efficiency optimization for peer-to-peer trading platforms considering heterogeneity of supply and demand sides

Impact of endogenous power factors and price marketization on agricultural energy efficiency: Based on the use of coal and oil energy in China

In the face of the enormous challenge of climate change and environmental pollution, improving energy efficiency is currently the best option for achieving economic green recovery. Using generalized method of moments estimation of dynamic panel data models, we examined the effects of endogenous power factors (e.g., farmers' human capital, invention patents, and agricultural digitalization) on agricultural energy efficiency, as well as the moderating role of price marketization therein. The results show that farmers’ human capital and agricultural invention patents cannot significantly improve agricultural energy efficiency, whereas agricultural digitalization promotes it obviously. Meanwhile, price marketization has played an important role in improving agricultural energy efficiency. Moreover, the higher the price marketization level, the more significant the role of endogenous power factors in improving agricultural energy efficiency.

Insurer Market Power And Hospital Prices In The US.

The extent to which concentration in the health insurance market affects negotiated prices paid to hospitals is of high interest to policy makers. We examined the association between insurer market share and hospital prices, using a new source of data obtained through the federal Hospital Price Transparency initiative. We found that the market-leading insurer in the least competitive (most concentrated) insurance markets pays 15percent less to hospitals than the market-leading insurer in the most competitive (least concentrated) markets. We also found the price relationship to be more pronounced for for-profit hospitals than for not-for-profit hospitals. Our results invite the question of whether dominant insurers are passing savings on to employers in the form of lower premiums.

Demand planning of a nearly zero energy building in a PV/grid-connected system

The energy consumption planning of smart homes connected to a grid with flexible power pricing, and complemented with a local PV installation, is studied. Advanced energy management is necessary to implement the Nearly Zero Energy Building (nZEB) concept in these smart homes. To do so, in the Energy Management System, it is essential to consider the flexibility provided by power pricing schemes, the distributed generation from renewable energies and the use of smart household appliances. This paper studies a novel formulation that depends on the comfort index required by the user, the PV power production, and the price variation of the grid energy. The study is performed on two levels: in the first, a metaheuristic multi-start, consisting of two steps, constructive heuristics and refinement heuristics (local search), is proposed to regulate the power demand of household appliances. The second level consists of determining the optimum PV installation that ensures the economic viability of the plant. The novelty of the paper is that the user defines a desired comfort level as input data, and the algorithm defines a demand plan that meets this comfort at the lowest possible cost. Six real case studies were carried out, considering the insertion of photovoltaic generation, to evaluate the performance of the proposed model from a technical and economic point of view. The results show that the proposed approach allows user requirements to be fulfilled and the energy production cost to be minimized. Without needing to reduce user comfort, the proposed demand planning algorithm can reduce the energy cost by 48.07 %. In this same scenario, combining the proposed method and the insertion of a photovoltaic system composed of 15 modules (Scenario 6), the total energy cost is reduced by 73.90 %.

Quantitative analysis and co-optimization of cathode catalyst layer compositions and operating conditions on cost and performance of PEM fuel cell

ABSTRACT Increasing the net power density of proton exchange membrane fuel cell without extra cost is beneficial to its commercialization. To this end, a one-dimensional, two-phase, non-isothermal model was developed in this work. The influences of cathode catalyst layer compositions including the mass ratio of ionomer to carbon (Ri/c) and mass ratio of platinum to carbon (Rpt/c) and operating conditions including temperature, cathode relative humidity (RHc), anode relative humidity (RHa), and operating voltage (V) on the cost and performance of fuel cell were quantitatively investigated by range and variance analyses based on an orthogonal test. The results indicate that Ri/c is the most significant factor affecting net power and unit power price. In addition, the RHc and V are also sensitive parameters to the net power, while Rpt/c affects the unit power price. Finally, with the consideration of the interaction between the operating conditions and the cathode catalyst layer compositions, a multi-objective optimization was conducted to improve the aforementioned two indicators. Compared to the base case, the optimized fuel cell achieves a net power increase of 50.8% and a 24.7% reduction in unit power price, which allows the realization of a high-performance, low-cost fuel cell.

Research on the environment & economic indicators for electricity trading

In this paper, the environmental and economic indicators based on the reactive power pricing are proposed so as to formulate the novel environmental economic power dispatch (NEED) problems for electricity trading. Here, a new way of calculating total cost is proposed based on the reactive power pricing instead of the pure fuel cost, which leads to the NEED problems. In addition, an improved tunicate swarm algorithm (ITSA) is proposed to deal with the NEED problems. Different from the original tunicate swarm algorithm (TSA), adaptive weight parameter and mutation process are used to improve the disadvantages of TSA which converges too fast and easily falls into local optimum. In combination with the Pareto dominance rule, the ITSA can be more effective in dealing with the NEED problems. In order to examine the performance of the ITSA, simulation tests of ITSA, TSA, and particle swarm optimization (PSO) are conducted in IEEE30 node system. During the experimental process, optimized objective functions include total cost considering reactive cost, pollution emission and power loss. The final comparison results show that the ITSA is able to get better distributed Pareto front (PF) and superior best compromise solution (BCS), which proves the high efficiency of the ITSA in dealing with the NEED problems.

Optimal Dispatching of Smart Hybrid Energy Systems for Addressing a Low-Carbon Community

A smart hybrid energy system (SHES) is presented using a combination of battery, PV systems, and gas/diesel engines. The economic/environmental dispatch optimization algorithm (EEDOA) is employed to minimize the total operating cost or total CO2 emission. In the face of the uncertainty of renewable power generation, the constraints for loss-of-load probability (LOLP) and the operating reserve for the rechargeable battery are taken into account for compensating the imbalance between load demand and power supplies. The grid-connected and islanded modes of SHES are demonstrated to address a low-carbon community. For forecasting load demand, PV power, and locational-based marginal pricing (LBMP), the proper forecast model, such as long short-term memory (LSTM) or extreme gradient boosting (XGBoost), is implemented to improve the EEDOA. A few comparisons show that (i) the grid-connected mode of SHES is superior to the islanded-connected mode of SHES due to lower total operating cost and less total CO2-eq emissions, and (ii) the forecast-assisted EEDOA could effectively reduce total operating cost and total CO2-eq emissions of both modes of SHES as compared to no forecast-assisted EEDOA.

A customized electricity pricing approach that considering the gaming ability of users and the new power sales company

The prevailing customized electricity pricing methods in China primarily utilize a single power price, which is an extra service cost charged per kWh. This paper suggests a customed power pricing optimization method that takes into account not only electricity quantity (kWh), but also capacity (kVA), and considers the gaming ability of both users and the electricity retail company. Firstly, the method for determining level of the quality of power is introduced in this paper, which is based on various quality of power indices Next, an optimization model for the customized electricity pricing is suggested, which takes into account the gaming ability of both users and the new power sales company (NPSC). Lastly, this paper conducts a case study of four representative users situated in an industrial park in Chengdu, Sichuan province to demonstrate the practical application of the proposed approach. The findings indicate that the suggested approach can allocate customized electricity charges between capacity and electricity consumed by users in a rational manner, thus enhancing the efficiency of production equipment for users.

Can power market reform reduce air pollution?--Evidence from prefecture-level cities in China.

The market-oriented reform of China's power market has gradually transformed power prices from government pricing to market regulation, which not only promotes the production efficiency of industrial enterprises, but also inhibits the excessive consumption and waste of power by residential power users. This paper uses the data from 2006-2018 combined with the precious industrial power price data and macroeconomic data of 100 cities in China, takes the marketization reform of the power market in 2015 as a quasi-natural experiment, and uses the difference-in-differences model to empirically study the causal relationship between power market reform and air pollution for the first time. The study found that power market reform can reduce air pollution, and this conclusion is also supported by a number of robustness tests. Mechanism analysis shows that power market reform can reduce air pollution by improving power market efficiency, promoting technological progress, and reducing power consumption. Heterogeneity analysis shows that power market reform can suppress air pollution more significantly in eastern regions, regions with severe air pollution, and regions with larger populations. This paper not only provides new research perspectives and research ideas for air pollution prevention and control, but also provides empirical evidence for the positive externalities of power market reform.

Distributed Economic Control Method for Energy Storage Unit with Random Time Delay

The operation cost waste in the charge and discharge process cannot be ignored for islanded microgrids with energy storage units. Different from the economic dispatch methods focused on the power pricing and bid coefficient in the tertiary control layer, this paper designed a power management algorithm for the economic operation of energy storage units in the secondary control layer. The strategy is devoted to minimizing the total charge power cost of energy storage units while satisfying the constraint of power balance. Meanwhile, the maximized allowable delay bounds were derived to guarantee the communication network reliability when against random time delays. The system stability was analyzed by the Lyapunov functions. Finally, the numerical simulations on the modified IEEE 30 feeder model verify the superiority of the provided algorithm.

A robust bi-level optimization framework for participation of multi-energy service providers in integrated power and natural gas markets

This paper presents a bi-level scheduling model for a new energy system under the concept of multi-energy service providers (MESPs) to participate in the integrated power and natural gas market (IPNGM). While the presented bi-level model takes full consideration of the unit commitment constraints of the power network and line pack constraints of the gas network into consideration at the lower level, the MESPs minimize the cost of purchasing power and natural gas by operating energy storage systems as well as the demand response program (DRP) as flexible technologies at the upper level. In order to solve the bi-level problem, an iterative-based two-step algorithm is developed. Moreover, since the MESPs cannot accurately predict other participants in the IPNGM, especially renewable energy sources (RESs), the power price determined by IPNGM is considered an uncertain parameter, and a robust optimization (RO) method is employed to capture this uncertainty. The proposal is formulated as a mixed-integer linear programming (MILP) and carried out on the IEEE 6-bus power system integrated with the 6-node natural gas network and considering one MES using the CPLEX solver in the general algebraic modeling system (GAMS) environment. Further, to show the model flexibility, simulation results are extended to the IEEE 118-bus power system integrated with the 10-node natural gas network by considering six MESs. The obtained results verified the effectiveness of the model by reducing the cost of the purchased power and natural gas up to 4.39% by employing flexible energy sources.

Machine learning optimization model for reducing the electricity loads in residential energy forecasting

Recently, households have consumed more power by overusing refrigerators, mobiles, washing machines, and electrical appliances. The incoming energy needs to be optimized, and this research uses machine learning for energy forecasting to reduce the overconsumption of power. Intelligent buildings are additionally associated with Heating, Air Conditioning (HVAC), and Ventilation Units. Due to the limited devices, it acquires only minimum communication capability. In this paper, deep learning with Meta-heuristic based algorithm has been proposed to address the constraints such as multi-objective optimization and fitness function and address the energy consumption of HVAC units. It uses a gated recurrent circuit (GRU) with a gorilla troop optimizer (GTO) to optimize the energy efficiently. This study uses the HVAC model to analyze smart buildings and addresses power consumption through HVAC systems based on power loss, price management, and reactive power. The simulation environment is performed using python at various experiments with various scenarios for proposed evaluation. Here, integrity in intelligent buildings is considered. The results outcomes are compared with other HVAC devices using different metrics and communication protocols. It has been proven that the proposed model is more effective in reducing the system's energy consumption than other approaches.

Reducing the consumption of household systems using hybrid deep learning techniques

At present household consumes high power due to over usage of mobiles, refrigerators, washing machine, and other electrical appliances. It is necessary to optimize the incoming energy and the main of this research is to use machine learning for forecasting the energy for reducing the overconsumption of power. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation, and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. For multi-objective optimization problems, the Pareto front solutions are produced using the Pascoletti-Serafini scalarization method. The suggested models produce predictions based on power usage using Hybrid Deep Learning models. Models such as Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) and Drop-CRU were used. To forecast upcoming load demand and avoid utilization peaks, the LSTM neural network has been preferred in this work. The study analyzes and optimizes the consumption of power in smart buildings by HVAC systems in terms of power loss, price management, and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed (PS-LSTM) system is considered more effective than other models.

Optimal reactive power pricing with transformer variable taps using genetic algorithm

<span lang="EN-US">Ancillary services in interconnected networks are crucial to secure real power transfers, maintaining network reliability, improving power quality as well as network stability. Reactive power has always been of special importance as one of the most substantial ancillary services required to control the voltage in power grid. This paper presents a fresh reactive power pricing approach by embedding variable transformer taps into voltage ampere reactive (var) pricing method. To carry out the resultant optimization problem with a set of complicating constraints, the genetic algorithm takes part and guarantees the global optimal solution. The simulation results show that when transformer tap works as a control variable, the total cost of reactive power can be decreased. However, the incorporation of transformer taps in the pricing model can slightly complicates voltage profiles as the sensitivity of bus voltages to the reactive power variations in the system is increased. Further, the findings reiterate that by optimal position of transformer taps, technically, the security index of the system can be enhanced while the var price for end-users (lowest purchase cost for independent system operator) being more reasonable. To simulate the proposed reactive pricing method, the standard IEEE 30-bus system is employed to analyze the proposed method.</span>

Development and performance evaluation of a water lifting device

The uninterrupted availability and price of electric power is a great solicitude for a common man; thus, irregular power availability for irrigation is a daily problem faced by rural farmers. In most rural communities, hand-operated water pumps are very predominant due to lack of electricity to power borehole water systems. Conventional energy is also a great concern and now, core attention of the people is being diverted towards the use of an alternate form of energy derived from human and animals. The need for water as a basic necessity in life has led to various developmental and engineering innovations that are designed to meet this need optimally. The water pump is the most useful machine, and it is the second most commonly used equipment by the farmers for irrigation purposes. An appropriate alternative to diesel pump, which requires neither diesel nor electricity, yet meets irrigation requirements, may be beneficial. With this, a simple hand-operated water-lifting device has been developed at CSIR-Central Mechanical Engineering Research Institute at Durgapur, West Bengal. Three prototypes were developed and evaluated successfully. From the results, it has been observed that, input effort has the largest effect on efficiency. The highest efficiency was found as 80% in case of prototype-III.

Analyzing the role of green innovation and public-private partnerships in achieving sustainable development goals: a novel policy framework.

The environment's quality is the cornerstone for every country's long-term growth. Pakistan, like other countries, is embracing modern, efficient technologies to build a sustainable environment following the SDGs. In this situation, policymakers and experts have emphasized more on environmental factors. To do this, the study explores the impact of green innovation (GI), public-private partnerships in energy (PPP), energy use (EU), economic development (ED), and power prices (PP) on CO2 emissions in Pakistan from 1980 to 2019. The research uses a novel econometric technique for estimating environmental factors, notably the dynamic autoregressive distributed lag simulations (ARDLS) model and spectral frequency domain causality (SFDC), to examine positive and negative shocks for the prediction of the short-, medium-, and long-run impact of selected determinants, respectively. Additionally, robustness checks were performed using the fully modified OLS (FMOLS), dynamic OLS (DOLS), and canonical cointegrating regression (CCR) estimations. The short and long-term empirical findings indicate that GI lowers emissions; nevertheless, PPP, EU, and ED have a significant impact on emissions in the short run, while the EU increases emissions in the long run. PP, on the other hand, reduces emissions both short and long-term. The FMOLS, DOLS, and CCR estimations indicate significant discoveries. Additionally, the SFDC finding supports the long, medium, and short-term causation theories. This research advocates green innovation for a greener manufacturing process and PPP investment in renewable energy. In addition, the Pakistani government considers these variables while designing a comprehensive protracted environmental plan to meet SDGs 7 and 13.