Demand Bids Research Articles

Demand bidding vs. demand response for industrial electrical loads

We investigate a novel optimal demand bidding model for industrial loads based on an extended optimal power flow problem for the day-ahead market. The objective function accounts for generation costs and revenue resulting from the sale of products made by the bidder using electricity. As a bidding entity, the industrial load participates in electricity price formation, and is described using a model whose parameters can be determined mostly from public information. Constraints specific to industrial loads, such as satisfying product demand, are also included. The model is validated using simulations of a modified IEEE 24-bus reliability test case with a chlor-alkali plant as the load entity. We find that the proposed participation model, which is simple enough to include in grid dispatch software, performs well relative to other demand-side management strategies such as price-based demand response and cooperative scheduling of industrial load by the grid operator.

A nonlinear hyperbolic optimized grey model for market clearing price prediction: Analysis and case study

Market Clearing Price (MCP) of an energy market is an operating equilibrium attained, when consumer demand and supply bid curves intersect with each other. Early anticipation of MCP provides an upper hand to any generating company to exercise the market power in its own favour. MCP prediction technologies are evolved in recent years and emphasis is given on developing a robust mechanism for anticipating dynamic conditions. This work presents an application of a nonlinear whitening term enabled grey model for prediction of the MCP. Detailed mathematical analysis and development of the model is presented. The proposed model is named as Nonlinear Hyperbolic Optimized Grey Model (NHOGM). For optimizing the whitening equation parameters, Linear Population Size Reduction (LPSR) Success History Based Parameter Adaption Differential Evolution (L-SHADE) algorithm has been applied. Results reveal that proposed nonlinear hyperbolic grey model yields satisfactory results as compared with the state of art grey methods and other well-known optimization based methods.

Aggregator-induced demand response and bidding strategy for electricity market environment based on Ethereum smart contracts

ABSTRACT With the liberalization of the Electricity Market (EM), a secure demand response program (DRP) for real-time decisions is needed to enhance the effectiveness, stability, and security of the EM. Customers play a crucial role in DRP as they can offer flexibility when the market demands it. Nevertheless, the aggregator does not fully understand the customer’s available DR capacity to accurately bid on market transactions. To do so, the article presents a smart contract based on an optimal bidding strategy for an aggregator that considers customer receptiveness. First, we endeavor to determine the customer’s receptiveness concerning various incentives where the home energy management system (HEMS) introduces the employed load adjustment for electrical appliances. Second, the aggregator adopts a rational approach to employing the optimal day-ahead bidding strategy and scheduling for energy storage systems (ESS) to exploit economic benefits. The execution of Ethereum smart contracts in market transactions ensures transparency, security, and superior reliability. A test case with realistic data is presented to demonstrate the usefulness of the proposed bidding strategy. The results confirm that the aggregator determines the optimal bidding value for the next day. An aggregator could generate a profit of $23 from the bidding strategy. The regional transmission system operator delivers 485 kWh of flexibility, of which consumers account for 60% and ESS for 40%, generating a profit of $40 per day. Prosumers selling electricity to the aggregator would make an average profit of $6 per day. Thus, the execution of DRP and a bidding strategy results in economic benefits for the aggregator and other entities in the EM.

Risk Assessment of User Aggregators in Demand Bidding Markets

This paper mainly discusses the demand bidding and risk management of user aggregators by considering profit and risk. The covariance matrix of demand price was used to analyze the risk model under an uncertain demand price. By considering revenue and cost, the demand bidding strategy of user aggregators was derived to obtain the maximum profit. By using a risk-tolerance parameter β, a new demand bidding model for the user aggregators that takes both risk and profit into consideration was formulated. We simulated the risk posed by fluctuating demand prices for user aggregators using this model. Finally, this paper proposes Feasible Particle Swarm Optimization (FPSO) to solve the demand bidding model of user aggregators. Through the dynamic adjustment of control factor parameters in the FPSO, we changed the behavioral characteristics of various types of particles, improved the search efficiency and stability of particles in high-dimensional space, and sought the optimal solution for the system as a whole. This paper provides a parameter adjustment mechanism, improves the capability of algorithm implementation, and increases the probability of finding the optimal solution. The simulation results suggest that a tradeoff between profit and risk needs to be considered in the search process. By doing so, enterprises’ abilities in terms of operation and management control can be enhanced, and effective demand management can be achieved.

Bidding strategy for wireless charging roads with energy storage in real-time electricity markets

The combination of wireless charging roads and energy storage systems is a promising option for electric vehicle charging because of their capabilities in mitigating range anxiety of electric vehicle drivers. Wireless charging road operators can purchase electric energy by submitting price-sensitive demand bids in real-time electricity markets. Efficient bidding strategies are crucial to minimizing the energy costs for providing wireless charging services. In this study, we first propose a composite statistical model based on graph signal processing and linear regression to forecast the future locational marginal prices (LMPs) in a power network. Then an estimate of future electric load on each wireless charging road is derived by simulating its traffic flow using a point queue-based traffic flow model. An efficient price-sensitive bidding strategy for each individual wireless charging road is developed based on its LMP forecast, wireless charging load estimate, and a model predictive control framework. Our numerical example shows that the proposed price-sensitive demand bidding strategy reduces the electric energy cost for operating a wireless charging road with an energy storage system by 6% compared to a baseline bidding strategy.

Transactive control of air-conditioning systems in buildings for participation in Singapore’s Demand Response market through load curtailment

Air-conditioning and mechanical ventilation (ACMV) systems account for significant electrical demand within buildings. These systems have large thermal time constants and can be utilized as thermal energy storage. This paper is focused on participation of commercial buildings in Singapore’s Demand Response (DR) programme through curtailment in electricity demand of ACMV systems for financial incentive and power grid benefits. A novel Transactive Energy-based virtual market is proposed for a building where multiple air-conditioning zone agents transact to purchase conditioned-air from central ACMV system. These zone agents consider forecasts of occupancy and outdoor temperatures to submit bids for conditioned-air corresponding to increasing electricity market prices, using a novel dynamic programming approach to utilize the temporal flexibility in cooling demand through pre-cooling. ACMV electricity demand bid is calculated based on net conditioned-air bids by using existing accurate building models in whole-building energy modeling (BEM) platforms corresponding to increasing electricity prices for submission to DR market. Case studies are performed in MATLAB–EnergyPlus co-simulation environment on a benchmark five-zone building model representing a small office building. Results show that the proposed control is effective in providing curtailment of ACMV electricity demand for DR according to real Singapore market price signals, while respecting occupants’ thermal comfort preferences and up to 56% reduction in ACMV electrical power demand is possible for 1 h. Further, revenue analysis shows that DR market participation incentive from 1 h participation would be higher than energy costs for ACMV operation for the entire day, which makes the DR market participation commercially lucrative.

Strategic retail pricing and demand bidding of retailers in electricity market: A data-driven chance-constrained programming

This paper proposes a novel bi-level optimization model to study the strategic retail pricing and demand bidding problems of an electricity retailer that considers the interactions between demand response and market clearing process. In order to accurately forecast the day-ahead demand bids submitted by the retailer, a novel deep learning framework based on convolutional neural networks and long short-term memory is proposed that can capture both local trends and long-term dependency of the forecasting data. In addition, uncertainties about the retailer’s served demand, rivals’ demand bids, and wind power generation are incorporated using the data-driven uncertainty set constructed from data. We further propose chance-constrained programming that introduces a set of chance constraints to represent the operational risk associated with the market uncertainties. To solve this problem, we first reformulate chance-constrained programming as a tractable second-order conic programming and then convert it into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker conditions. The scope of the examined case studies is four-fold. First, they evaluate the benefits of the proposed forecasting framework in terms of higher accuracy and expected profit compared to the conventional forecasting methods. Second, they demonstrate how demand flexibility affects the retailer’s strategies and its business cases. Third, they highlight the added value of the proposed bi-level model capturing the market clearing process by comparing its outcomes against the state-of-the-art bi-level model with exogenous market prices. Finally, they analyze the retailer’s strategies and business cases at different confidence levels regarding the imposed chance constraints.

Effect of Demand Response Programs on Industrial Specific Energy Consumption: Study at Three Cement Plants

Demand-side management (DSM) is the modification of consumer utilization manner for energy through various methods to smooth their power consumption curve and increase their energy efficiency. Although the DSM improves the electricity grid stability and protects the environment, it allows customers to reduce their costs. The available literature classifies DSM into two different areas: (i) energy efficiency (EE) and (ii) demand response (DR). In other words, cement plants are the largest customers of electricity, so the implementation of DSM programs in these heavy industries is very important for electricity companies. Past studies have often examined the impact of DR programs in cement factories on solving peak problems and changing load profiles but have not studied the effects of DR implementation on the energy efficiency index in the short and long term in these industries. In this paper, while examining the effect of implementing three types of common DR programs in smart grids including demand bidding (DB), ancillary services (A/S), and time of use (TOU) on the load profile of cement plants, based on the mathematical model of the load and the real data of the case study, it is shown that the implementation of the TOU program has created the highest energy efficiency by reducing specific energy consumption (SEC) in cement plants.

Data-driven online interactive bidding strategy for demand response

Demand response (DR), as one of the important energy resources in the future’s grid, provides the services of peak shaving, enhancing the efficiency of renewable energy utilization with a short response period, and low cost. Various categories of DR are established, e.g. automated DR, incentive DR, emergency DR, and demand bidding. However, with the practical issue of the unawareness of residential and commercial consumers’ utility models, the researches about demand bidding aggregator involved in the electricity market are just at the beginning stage. For this issue, the bidding price and bidding quantity are two required decision variables while considering the uncertainties due to the market and participants. In this paper, we determine the bidding and purchasing strategy simultaneously employing the smart meter data and functions. A two-agent deep deterministic policy gradient method is developed to optimize the decisions through learning historical bidding experiences. The online learning further utilizes the daily newest bidding experience attained to ensure trend tracing and self-adaptation. Two environment simulators are adopted for testifying the robustness of the model. The results prove that when facing diverse situations the proposed model can earn the optimal profit via off/online learning the bidding rules and robustly making the proper bid.

Stochastic local flexibility market design, bidding, and dispatch for distribution grid operations

In order to unlock the flexibility potential of energy consumers and prosumers, the development of market mechanisms for flexibility planning and procurement is necessary. The authors propose a stochastic local flexibility market to solve grid issues such as voltage deviations and grid congestion in a distribution grid. Their proposed solution includes activation of flexibility assets at the consumers’ premises, using a stochastic local flexibility market design. They consider a pooled local flexibility market design under demand uncertainty and stochastic bidding process. Optimization models are used to determine flexibility demand and supply bids by the distribution system operator and the aggregator respectively. A stochastic AC-optimal power model to determine flexibility demand and a two-stage stochastic model to supply flexibility are implemented to simulate a stochastic local flexibility market. This allows to determine stochastic flexibility supply bid curves, and optimum flexibility supply dispatch. The analysis shows that the cost of grid operations is reduced when the system uses the local flexibility market. The proposed methodology is applicable for local flexibility market designs aiming to use potential end-user flexibility for grid operations.

On wholesale electricity prices and market values in a carbon-neutral energy system

Climate and energy policy targets of the European Commission aim to make Europe the first climate-neutral continent by 2050. For low-carbon and net-neutral energy systems primarily based on variable renewable power generation, issues related to the market integration, cannibalisation of revenues, and cost recovery of wind and solar photovoltaics have become major concerns. The traditional discussion of the merit-order effect expects wholesale power prices in a system with 100% renewable energy sources to alternate between very high and very low values. Unlike previous work, we present a structured and technology-specific analysis of the cross-sectoral demand bidding effect for the price formation in low-carbon power markets. Starting from a stylised market arrangement and by successively augmenting it with all relevant technologies, we construct and quantify the cross-sectoral demand bidding effects in future European power markets with the cross-sectoral market modelling framework SCOPE SD. As the main contribution, we explain and substantiate the market clearing effects of new market participants in detail. With this, we focus on hybrid heat supply systems consisting of combined heat and power plants, fuel boilers, thermal storage, and electrical backup units and derive the opportunity costs of these systems. Furthermore, we show the effects of cross-border integration for a large-scale European net-neutral energy scenario. Finally, the detailed information on market clearing effects allows us to evaluate the resulting revenues of all major generation and consumption technology categories on future electricity markets.

Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy

In this paper, a risk-constrained stochastic mixed-integer linear programming (MILP) model is proposed for optimal bidding strategy of a grid-connected CHP-based multi-microgrid (MMG) system in energy and reserve markets considering environmental restrictions. A reward-based demand response program is considered in the proposed framework to realize demand-side management and boost the economic performance of the MMG system. Besides, conditional value-at-risk as a proper risk-averse index is incorporated into the developed mixed-integer programming formulation to hedge the risk of the low profits associated with worst scenarios. For this study, the uncertainty of energy market price, electrical load, and power generation of renewable energy sources are taken into account through employing stochastic programming. Meanwhile, given the global policy for emissions reduction, an emission constraint is considered into the model, ensuring a more green trading strategy. The obtained simulation results over a 24-h time horizon, demonstrate that increasing the risk factor leads to the profit decrement by almost 30.37% while the conservativeness of the MMG against the risk is improved. Also, the results reveal that by increasing the value of emission factor, the system's profit is decreased by about 30.03% while the MMG would be operated more environmental-friendly.

Optimal Power Flow in Deregulated Power Systems by Using Optimization Techniques

An independent system operator tasked with providing equitable and fair transmission services in an open-market context has a challenging job in dynamic security-constrained dispatch of an electric power network. This study proposes a new methodology based on the optimal flow of power and swarming mode, which is restricted by iterative stability. In addition to the static and dynamic functional constraints, dynamic margin needs in relation to normal condition and contingencies, particle swarm and moth swarm optimizations are used to optimize social welfare. Furthermore, because the pattern of load growth in the current market environment is difficult to forecast, a new approach for estimating the sensitive loading direction linked with a dynamic loading margin is presented. To demonstrate and test the suggested approach, an IEEE 14-bus test system with both supply and demand bids is utilized.

EFECTOS A CORTO PLAZO DEL CIERRE DE LAS CENTRALES TÉRMICAS DE CARBÓN EN EL MERCADO ELÉCTRICO ESPAÑOL

ABSTRACT: This article analyses the effect of the closure of the Spanish coal-fired power stations on the price and quantity of energy sold in the daily electricity market. The comparative statics analysis is based on the hourly data of energy offer and demand bids published by the OMIE (Operator of the Iberian Energy Market) and made by the participants in the daily electricity market during the year 2018. Our analysis does not require any simulation of the market supply and demand curves, since they are obtained by aggregation using real data from the wholesale market. The main conclusion of our analysis is that, after the closure of the coal-fired stations, the hourly price of energy would increase 12,06% on average while the average amount of energy would decrease 2,57%. Keywords: Price of electricity; coal plants; ecological transition; supply and demand surpluses; renewable energy.

Flexible block offers and a three-stage market clearing method for distribution-level electricity markets with grid limits

This paper proposes a new flexibility management method which can offer incentives to flexibility service providers so that they are encouraged to actively participate into the congestion management of distribution grids. The proposed method consists of a three-stage market clearing method and three types of flexible block offers: flexible demand bids, flexibility service offers, and energy arbitrage blocks. These cover most of the needs of flexibility providers. These flexible block offers are more efficient in terms of computation burden in comparison with the other flexibility offers in the existing literature. Flexibility prices are specified in these flexible block offers, which can guarantee a certain level of rewards for the customers who can help in the congestion management process. Case studies on a modified Roy Billinton Test System demonstrated the efficacy of the proposed flexibility management method for managing congestion and encouraging customers to provider flexibility services.

Effects of a Battery Energy Storage System on the Operating Schedule of a Renewable Energy-Based Time-of-Use Rate Industrial User under the Demand Bidding Mechanism of Taipower

Due to the increased development of the smart grid, it is becoming crucial to have an efficient energy management system for a time-of-use (TOU) rate industrial user in Taiwan. In this paper, an extension of the direct search method (DSM) is developed to deal with the operating schedule of a TOU rate industrial user under the demand bidding mechanism of Taipower. To maximize the total incentive obtained from the Taiwan Power Company (TPC, namely Taipower), several operational strategies using a battery energy storage system (BESS) are evaluated in the study to perform peak shaving and realize energy conservation. The effectiveness of the proposed DSM algorithm is validated with the TOU rate industrial user of the TPC. Numerical experiments are carried out to provide a favorable indication of whether to invest in a BESS for the renewable energy-based TOU rate industrial user in order to execute the demand bidding program (DBP).

Simulated Annealing Algorithm for Dynamic Economic Dispatch Problem in the Electricity Market Incorporating Wind Energy

In this paper, a bid-based dynamic economic dispatch (BBDED) problem is solved in the electricity market system under bidding strategies, including wind energy penetration using simulated annealing (SA) algorithm. The multi-objective dispatch model allows generating companies (GENCOs) and their customers to submit supply and demand bids to a market controller known as the independent system operator (ISO) and follow a bidding strategy. ISO is responsible for the market clearing and settlement to maximize the social profit and benefit for GENCOs and customers during trading periods. To study the effect and advantages of wind energy integration in the BBDED problem, the wind energy generation is computed using the forecasted wind speeds and included in the dispatch model. In this regard, the ISO's dispatch model is formulated as a bilevel nonlinear optimization problem. The higher-level is solving the market-clearing with and without wind energy, and the lower level is maximizing GENCO's social profit. The proposed SA algorithm is evaluated for optimality, convergence, robustness, and computational efficiency tested on an IEEE 30-bus test system. The simulation results are compared with those found using different algorithm-based approaches, considering various constraints like power balancing, generator limits, ramp rate limits, and transmission losses.

Measuring Demand Elasticity in the Italian Power Market: a Bayesian Experiment under dual pricing scheme

This study run experiment aiming to provide a flexible and analytically elegant framework to reliably estimate the price elasticity of electricity demand. Inference pertains to the demand at hourly level in the Italian wholesale electricity market and uses individual demand bid data. Individuals' bids represent the ex-ante willingness to pay and thus allows for constructing a market demand grounded in the consumer behavior theory, by exploiting the duality approach. Bayesian econometric estimation is applied, relaxing homoskedasticity assumptions of the traditional linear regression model. It allows to identify robust results, showing that elasticity varies significantly among hours of the day, zone segmentation as well as the level of equilibrium price. Bayesian inference provides also the opportunity to include prior information sourced from previous studies and the institutional struc- ture governing the agents' behavior. This prior information involves some degree of uncertainty, for this reason Bayesian approach assigns it a probability distribution. Using Bayes rule, prior information are then updated according to the observed data. Results validate the market reform designed to foster competition and increase wel- fare even through the time-varying pricing schemes that trigger the consumers' price reaction.

Demystifying market clearing and price setting effects in low-carbon energy systems

Abstract The pursuit of climate stabilisation scenarios requires large-scale decarbonisation of power, heat, industry, and transport sectors. Cost-efficient climate neutrality strategies will primarily involve direct electrification of final energy demands, based on variable production from wind and solar PV as the primary source of renewable energy. Cross-sectoral integration is going to increase traditional electricity demands and is also expected to contribute with valuable flexibility in future electricity markets, particularly through bi- and multivalent technologies. However, the nature of market clearing and price formation effects in wholesale electricity markets with large amounts of renewable energy and significant cross-sectoral integration remains unknown. In this paper, we show and quantify that cross-sectoral demand bidding is going to be crucial for the price formation in future electricity markets. We found in a large-scale cross-sectoral capacity expansion and long-term cross-border transmission expansion study for the future integrated European system that bi- and multivalent cross-sectoral electricity consumers will be making market bids based on their valid opportunity costs. These price-setting technologies mainly include direct resistive and heat pump heating units. Unlike previous work, we can show and quantify that there will likely not be too many zero or even negative price situations due to excess renewable production in the system since the additional cross-sectoral consumers will, depending on their actual flexibility potential, use low-carbon electricity to supply final energy sector demands. Our results demonstrate how important it is to adequately capture cross-sectoral interactions and a variety of technology combinations in a high spatio-temporal solution when analysing future market clearing and electricity price formation effects in low-carbon energy systems. We anticipate our contribution to be a starting point for more sophisticated models incorporating detailed representations of cross-sectoral flexibility to investigate low-carbon energy markets further.

Assessing the impact of a demand-resource bidding market on an electricity generation portfolio and the environment

Although numerous studies have examined the economic benefits of demand response programs, the environmental impacts of such programs have been relatively underexplored. This study assesses the impact of demand resource bidding on the wholesale energy market and the environment, based on three years of high temporal-resolution data from Korea. In this demand resource bidding program, successful bidders were paid the system marginal price for reducing their electrical load at a given hour, which in turn reduced the generation of power from various technologies. This investigation of how carbon dioxide and particulate matter emissions from existing power systems changed with the introduction of the demand bidding program finds that the program altered the system operator's electricity generation portfolio and marginally abated carbon dioxide and particulate matter emissions from the power sector. It also shows that the environmental impact of the program varied over the course of the day and the year. The modest but statistically significant environmental impact of the demand resource bidding program points to the importance of including electricity demand resources in the discussion and development of energy and environmental policies for the power sector.