Power Price Research Articles

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.

Multi-Time-Scale Coordinated Optimum Scheduling Technique for a Multi-Source Complementary Power-Generating System with Uncertainty in the Source-Load

An optimal dispatching strategy for a multi-source complementary power generation system taking source–load uncertainty into account is proposed, in order to address the effects of large-scale intermittent renewable energy consumption and power load instability on power grid dispatching. The uncertainty problem is first converted into common situations for study, such as load power forecasting and solar and wind power. The backward scenario reduction and Latin hypercube sampling techniques are used to create these common situations. Based on this, a multi-timescale coordinated optimum scheduling control method for a multi-source complementary power generation system taking the demand response into account is presented, and the optimal operation of a wind–PV–thermal-pumped storage hybrid system is examined. The time-of-use power price optimizes the electrical load in the day-ahead pricing mode, and the two types of demand response loads are selected in the day-ahead scheduling. Second, the lowest system operating cost and the minimal day-ahead and intra-day adjustment of each source are established as the optimization targets in the day-ahead and intra-day phases of the multi-timescale coordinated scheduling model of the multi-source complementary system. The example study demonstrates that the scheduling strategy may increase the amount of renewable energy consumed, minimize load fluctuations, increase system stability, and further reduce operating expenses, proving the viability and efficiency of the suggested strategy.

A New Distributed Robust Power Control for Two-Layer Cooperative Communication Networks in Smart Grids with Reduced Utility Costs

The packet loss during transmission of load control commands can lead to regulation errors in the smart grid and increase the cost of utility agencies due to the purchase of additional automatic generation control (AGC) services. In this paper, a two-layer cooperative communication network between the utility company and relays is presented. The utility company rents the relay to assist with the downlink transmission to improve the reliability of communication and reduce the data transmission cost due to packet loss. Furthermore, the uncertainty of channel gain is considered, and a two-tier game model is established. A distributed robust power control algorithm based on the continuous convex approximation method is proposed to obtain the optimal relay power allocation and price. Through the simulation analysis of the proposed scheme and the two comparison schemes, the cost of the utility company was reduced by 6% and 21%, and the standard deviation of income value between the relays was reduced by 40% and 48%, respectively.

Coordination Decision-Making for Intelligent Transformation of Logistics Services under Capital Constraint

The intelligent transformation of logistics plays a significant role in meeting the diverse needs of customers, improving operational efficiency, and reducing carbon emissions in logistics activities. Therefore, to achieve sustainable development, logistics enterprises need to face the decision-making problem of intelligent logistics transformation. In this paper, we construct a Stackelberg game model between a financially constrained logistics-service provider (LSP) and a well-funded logistics-service integrator (LSI) and discuss the impact of the wholesale price contract, the cost-sharing contract, the revenue-sharing contract, the two-part tariff contract, and the hybrid cost-sharing and revenue-sharing contract on the intelligence level of logistics services, the profits of supply-chain members, and the channel for logistics-service demand. We found that the cost-sharing contract and the revenue-sharing contract cannot achieve Pareto improvement in the profits of supply-chain members. In addition, the increase in bank-loan interest rates would seriously weaken the level of intelligence and market demand for the entire logistics service. However, when consumers do not have high requirements for the intelligence of logistics services, the two-part power–price contract can create a win–win situation for supply-chain members and increase market demand within a certain range; on the contrary, a hybrid contract of cost sharing and revenue sharing is the best choice. Moreover, in the process of contract design for the intelligent transformation of logistics services, it is necessary to pay attention to the influence of the price-sensitivity coefficient on decision-making.

International factor models

We evaluate the relative and absolute performance of competing factor-based asset pricing models in international regions and globally. Our holistic analysis controls for model transaction costs and incorporates both right-hand-side tests (based on maximum squared Sharpe ratios) and left-hand-side tests (individual return predictors, composite mispricing proxies). The overall view of the tests shows that recently proposed models tend to perform better than classical models, but otherwise perform comparably. This finding, the performance of the models in some of the LHS tests as well as further results collectively suggest the need for new powerful asset pricing models for global equity markets.

Bi-layer game method for scheduling of virtual power plant with multiple regional integrated energy systems

Regional integrated energy system (RIES) can be connected with the virtual power plant (VPP) through power tie line, and participate in the VPP demand response dispatch. In order to improve the benefit of VPP and RIES, a bi-layer game method for scheduling of VPP with multiple RIES is proposed. The upper layer is the master-slave game between VPP and RIES-coalition. VPP guides RIES-coalition to formulate purchase and sale power quantity strategy, in response to the power demand scheduling of VPP by the power purchase and sale price incentive policy. The lower layer is cooperative game among RIES-coalition members. Within the RIES-coalition, a multiple energy optimization and coordination scheduling strategy was established to meet the “electrical/ heating/ gas” load demand of each RIES, and cooperation benefits are shared among members based on Shapley value method. CIQPSO algorithm is used to solve the Nash equilibrium point of the game model, namely, the optimal power price strategy of VPP and the optimal purchase and sale power quantity strategy of each RIES. The results of example show that the proposed strategy can improve the peak shaving and valley filling effect of VPP, maximize the benefit of RIES-coalition, and ensure the reliable operation of VPP.

A review of recent studies on valve-less piezoelectric pumps.

Due to the advantages of small size, low power consumption and price, no wear, and reliable performances of valve-less piezoelectric pumps, which academics have studied and gained excellent consequences for, valve-less pumps are applied in the following fields: fuel supply, chemical analysis, biological fields, drug injection, lubrication, irrigation of experiment fields, etc. In addition, they will broaden the application scope in micro-drive fields and cooling systems in the future. During this work, first, the valve structures and output capabilities of the passive valve and active valve piezoelectric pumps are discussed. Second, the various forms of symmetrical structure, asymmetrical structure, and drive variant structure valve-less pumps are introduced, the working processes are illustrated, and the advantages and disadvantages of pump characteristics with the flow rate and pressure are analyzed under different driving conditions. In this process, some optimization methods with theoretical and simulation analysis are explained. Third, the applications of valve-less pumps are analyzed. Finally, the conclusions and future development of valve-less piezoelectric pumps are given. This work attempts to provide some guidance for enhancing output performances and applications.

신재생발전량 확대에 대한 실증분석

Factors affecting the change in the amount of renewable power generation of KEPCO's power generation subsidiaries were empirically analyzed. For empirical analysis, a general power generation fuel demand model was established, and panel regression analysis was attempted using data from power generation subsidiaries for the period 2011-20. The empirical analysis results are summarized as follows. First, it is estimated that when the relative price of new and renewable power is high, the amount of renewable power is estimated to decrease. Second, renewable power generation has a substitute relationship with bituminous coal power generation and a complementary relationship with LNG power generation. have. Third, the RPS system directly and significantly played a leading role in increasing the amount of renewable power generation of power generation subsidiaries. Fourth, the emission trading system does not appear to have a significant effect on the increase in the amount of renewable power generation of power generation subsidiaries, which is why the emission trading system is not effective for renewable power generation. Fifth, the volatility of renewable power generation had a negative effect on the amount of renewable power generation as the supply of new and renewable power generation facilities increased. It can be inferred that as the amount of renewable power increases, the volatility of power generation increases, putting a burden on power generation subsidiaries.

Strategic active and reactive power scheduling of integrated community energy systems in day-ahead distribution electricity market

The increasing penetration of distributed energy resources (DERs) enable integrated community energy systems (ICESs) as emerging techno-economic energy entities at the end-user side. In the deregulated electricity market, the ICESs can actively participate in the distribution electricity market (DEM) to provide active and reactive power dispatched by ICES operator (ICESO). In this context, a strategic active and reactive power scheduling model for ICESs in the day-ahead DEM is proposed in this paper. A novel trading mechanism designed for ICESO and distribution system operator (DSO)’s interaction toward both active and reactive power is represented in the day-ahead DEM, where distribution locational marginal prices (DLMPs) for both active and reactive power are introduced as the market settlements. Then, the trading process is formulated as a bi-level programming problem. The upper level describes the combined cooling, heating, and power (CCHP)-dominated ICESO’s strategic active and reactive power dispatch, in which the DLMPs for active and reactive power, internal flexibility services, and optimal dispatch for inverter-based distributed generators are considered. The lower level performs a DEM clearing for both active and reactive power. Further, Karush–Kuhn–Tucker (KKT) conditions, Big-M approach, and duality theory are used to convert the bi-level model from a Mathematical Programing with Equilibrium Constraints (MPEC) model to a single-level mixed-integer second-order cone programming (MISOCP) model for efficient calculation. Finally, case studies on modified IEEE 33-node and IEEE 123-node distribution systems are adopted to evaluate the performance of the proposed scheduling method. The results show that the proposed scheduling approach can contribute to the renewable energy share and energy efficiency, reduce CCHP-dominated ICESs’ operation costs by 38.13% and 13.21% compared to the case where ICESO only acts as a price-taking consumer, and 6.64% and 3.44% compared to the case where ICESO does not consider reactive power bids/offers, respectively.

Network flexibility regulation by renewable energy hubs using flexibility pricing-based energy management

This article presents the energy management of electrical and thermal networks in the presence of renewable energy hubs to regulate the flexibility of these networks based on flexible pricing services. Hubs consist of renewable sources and bio-waste units in addition to energy storage and responsive loads. The bio-waste unit produces electrical and thermal energy simultaneously. The proposed scheme minimizes the difference between the network's energy cost and the hub's flexibility income. It is subject to the optimal power flow equations and flexibility limitation in networks, operation model of resources, storage, and responsive loads in form of energy hub, and hub flexibility model. Unscented transformation-based stochastic optimization models uncertainties of load, renewable power, and energy price. Finally, the numerical results demonstrate the capability of the proposed scheme in extracting the optimal state of flexibility, operation, and economic situation of electric and thermal networks with optimal energy management of renewable hubs based on flexible pricing services. The inclusion of renewable resources, storage generators, and responsive loads into a renewable hub improves the economic situation by 32.9% and the status of operation indicators of the energy networks by 22.4–42% in condition of 100% flexibility.

Coupling Mechanism and Synergic Development of Carbon Market and Electricity Market in the Region of Beijing–Tianjin–Hebei

The national carbon emission trading mechanism is an important policy tool for the Chinese government to control and reduce greenhouse gas emissions by using the market mechanism. The Beijing–Tianjin–Hebei power market is the focus of energy conservation and consumption reduction in China. Problems have already existed in the synergic development of the Beijing–Tianjin–Hebei power market and carbon trading market. In this article, the development status of the Beijing–Tianjin–Hebei power market is analyzed and the coupling mechanism between the carbon market and power market is combed out to build a synergism model of the carbon market and the Beijing–Tianjin–Hebei power market based on the system dynamics. From the research results, firstly, the Beijing–Tianjin–Hebei power market comes with a high energy consumption intensity and a high proportion of carbon emissions. The coupling of carbon market and power market forces the power industry to reduce carbon emissions through the effective transmission of carbon costs to power prices. Secondly, carbon price shows an upward trend in the context of the current policy scenario, which can give play to the role of price signal in the future. The revenue of thermal power plants, which are the carbon emission right sellers, with new technologies, has increased significantly, while the revenue of carbon emission right buyers, which are the manufacturers of undeveloped units, has increased less. Finally, the technical progress of thermal power plants, the introduction of auction mechanism, the increase in initial carbon price settings and the direct transmission of carbon costs are all factors that promote the effectiveness of carbon trading policy tools in the Beijing–Tianjin–Hebei power market. This study provides theoretical guidance for the synergic development of the “power-carbon” market.

Power transaction game algorithm with microgrid based on residual regression model

AbstractDirect transaction between the microgrid and distribution network is the most common market transaction mode. With the rapid expansion of business scale, industry development and diversification of service types, it is easy to cause problems such as opaque transaction data between users and easy tampering of transaction data. To improve the trading ability of the power market in the microgrid group, a game algorithm of power trading with microgrids based on a residual regression model is proposed. According to the power quality level and power sales strategy, a residual regression model is established to balance the characteristic quantity of electricity price. The quadratic function is used to solve the optimal selling strategy of power sales companies, and the threshold of equilibrium solution is analysed. The supply and demand model of the microgrid is established to optimize the decision variables of electricity price in power sales companies, and the fitness value is obtained by particle swarm optimization. The bidding strategy game model of the microgrid power sales company is constructed, and the rules of power transaction settlement are set to realize the transaction settlement between the microgrid and distribution network. The experimental results show that the electricity price is stable, the comprehensive income is high, the user income and cost income are moderate and the profit is high. Thus, it is proven that the proposed method is economical and effective, and the economy of electric energy use is guaranteed while fully considering the self-interest of microgrids.

Process as a battery: Electricity price aware optimal operation of zeolite crystallization in a continuous oscillatory baffled reactor

The electrification of chemical processes links the production costs and the CO2-footprint of the process industry more and more tightly to the price of electric power and the availability of power from renewable sources. Both for economic and for ecologic reasons, it is desirable to adapt the consumption to the availability of power from renewables. This work considers the optimal dynamic operation of a zeolite crystallization plant in a continuous oscillatory baffled reactor. The demand-side management problem is first investigated using a steady-state model. For the dynamic optimization problem, a moving horizon approach is introduced, and the effect of the terminal sell-off on the dynamic optimization is minimized by the usage of a terminal cost and additional horizons. The performance of the scheme is tested using different scenarios for the electric energy prices. Additionally, the influence of the uncertainty of the predictions of the price of electric power is addressed.

Resource Allocation in Macrocell–Femtocells via Combined Rain and Whale Optimization Algorithm

Femtocell (FC) dense exploitation imposes a novel challenge for the resource allotment in a 2-tier network, like: (1) dynamic resources allotment among the 2 tiers by considering the off-load traffic from macrocell (MC), (2) permitting access to users when ensuring QoS for FC subscribers, and (3) suitable power setting, which discovers cooperation among the overall bandwidth allotted to FCs and system performances. This research work examines the problems existing on cross-tier interfering as well as resource allocation alleviation in “full-duplex (FD) Orthogonal Frequency Division Multiple Access (OFDMA) oriented Heterogeneous Networks (HetNets), which involves macrocell with underlying femtocells”. Here, three primary contributions are concerned: a single objective issue together with power allocation, price allocation & subcarrier allocation of MC-FC networks. In addition, this research establishes a new hybrid algorithm termed Hybrid Whale and Rain Optimization (HW-RO) for solving the optimization problem in MC-FC Networks. Finally, the superiority of the suggested technique is evaluated in comparison to various existing approaches.

The current developments and future prospects of solar photovoltaic industry in an emerging economy of India.

Solar photovoltaic (PV) is a novel and eco-friendly power source. India's vast solar resources present tremendous solar energy use prospects. The solar PV growth in India has spanned over fifty years, with a significant increase during the past decade. To meet the requirements of the rapidly expanding PV power market in India, it is essential to define, scrutinize, and comprehend the industry's development path and features. This study comprehensively analyzes the current state of solar resources, the future growth prospects of the solar PV sector, and the major factors that influence the industry's smooth growth. The study relates to the following five major factors: technological R&D, industrial planning, rules and regulations, power pricing guidelines, and projects enticement programs. To explore these elements, a multifaceted approach consisting of an inclusive literature research, statistical data inquiry, legislation review, and regulatory and policy analysis is undertaken. Analyzing the usual occurrences, the development process, and the features of the five elements permits the growth of development route models. The findings of this study provide information regarding the current development and future prospects of PV power sector in India. In today's rapidly changing social, economic, and technological landscapes, policymakers have the opportunity to gain better understanding from the ever-evolving practices and new advances in the business paradigm.

Robust bidding strategy of battery energy storage system (BESS) in joint active and reactive power of day-ahead and real-time markets

The most important applications of an Energy Storage System (ESS) in power systems are energy arbitrage along with procurement of Ancillary Services (ASs). In addition to economic benefits, ESS also improves network reliability and stability. In this paper, a bidding strategy model of a Battery Energy Storage System (BESS) in a Joint Active and Reactive Power Market (JARPM) in the Day-Ahead-Market (DAM) and the Real-Time-Market (RTM) using a robust framework is presented. In this study, the BESS model is considered a price-taker, with the private owner trying to maximize its profit while facing price uncertainty. In the first stage of the proposed model, JARPM is cleared in a deterministic way without the presence of BESS to determine hourly prices of active and reactive power. In the second stage, by determining the prices uncertainty interval and the robust budget for DAM and RTM, the robust bidding model aims to maximize the BESS owner's profit, using the robust counterpart formation of the objective function and dual theory. The proposed robust formulation based on a Mixed Integer Non-Linear Programming Model (MINLP) is implemented in the GAMS software environment on the IEEE-24-RTS test system. The result shows that by the participation of BEES in the market, the average price of active and reactive power is reduced by 2 % & 3 % respectively. In addition, the overall cost of DAM execution is reduced markedly. On the other hand, the proposed framework in three case studies also guarantees a suitable profit level for the private owners of BESS (30–35 $/hour).

Optimal operation of multi-agent electricity-heat-hydrogen sharing in integrated energy system based on Nash bargaining

The P2P transaction mode has significant potential in improving energy utilization, increasing economic efficiency, and promoting low carbon operation of the system. With the development of integrated energy system, the coupling between multi-energy systems is continuously strengthened, and the study of multi-energy P2P transactions is of great significance. Considering the characteristic that natural gas can blend with hydrogen, this paper proposes a multi-agent electricity-heat-hydrogen trading model by taking hydrogen produced on the load side as a P2P transaction object. Based on Nash bargaining theory, the multi-energy transactions model is equivalent to a cooperative game model. Then, the Nash bargaining model is transformed into two continuous subproblems of minimizing operating cost and maximizing transaction payment. The optimal cost of each agent operating independently is used as the negotiation rupture point, and the alternating direction multiplier method is used for sequential solution to obtain the multi-energy trading power and trading price, respectively. Finally, the effectiveness of the proposed method is verified by using the distribution network IEEE 33-bus and natural gas 11 node systems. The results show that the revenue of subject 1 is increased by 11.9%, and the operating costs of the other subjects are reduced by 3.4%, 2.7% and 3.1%, respectively. The overall carbon tax cost of the system is reduced by 1 196.59 CNY, which can effectively reduce the carbon emission of the system.

Energy saving optimization of thermal power co-generation automation system in power plant

In order to establish a new optimal load distribution between CHP units and wind turbines and realize a new type of energy-saving dispatching, the authors proposed a dispatching system based on smart grid. The author proposed that the heat pump with electric drive air conditioning should share part of the heating load of the hot water radiator, accordingly, the supply of heating water is reduced, increase heating power load. This directly leads to the increase of the total power load in the grid and the decrease of the total heating and hot water load, changing the proportion of thermal power load. Based on the new heating water and power load constraints, a mathematical model of optimal scheduling is established, the energy-saving dispatching of cogeneration units and wind turbines is realized. The simulation results show that using the new energy-saving scheduling method, 342.4 MWh of fuel can be saved per hour, and the energy saving benefit is about 8.83%. If the calorific value of standard coal is 29271 kJ/kg, this means that the fuel consumption savings per hour is about 42.14 ton standard coal. The calculation results show that the higher the value is, the more economically feasible the feed-in price of wind power is. In conclusion based on the current electricity price and heating heat price, in order to ensure that the economic benefits of each participant are not changed, the feed-in price of wind power is discussed, and the economic feasibility of the method is proved.