Showing papers on "Bidding published in 2021"
TL;DR: A P2P energy trading framework enabled by blockchain consolidates bilateral contracts, an electronic-commerce platform, a double-auction Vickrey-Clarke-Groves (VCG) mechanism, and trading functionalities with the main grid, described as multi-settlement and quasi-ideal.
Abstract: The concept of peer-to-peer (P2P) trading, or transactive energy (TE), is gaining momentum as a future grid restructure. It has the potentials to utilize distributed energy resources (DERs), proactive demand side management (DSM), and the infusion in information and communication technologies (e.g., blockchain and Internet of Things (IoT)) for promoting the technical and economic efficiency of the system in its entirety. An efficient market framework is vital for the successful and sustainable implementation of such a concept. This article proposes a P2P energy trading framework enabled by blockchain. It consolidates bilateral contracts, an electronic-commerce platform, a double-auction Vickrey-Clarke-Groves (VCG) mechanism, and trading functionalities with the main grid. Through these multi-layer mechanisms, various trading preferences and attributes of electricity generation and/or consumption are accommodated. Meanwhile, the VCG mechanism eliminates any potential for market power exercise via incentivizing truthful bidding of participants. Different remedies are proposed to overcome the drawback of VCG, i.e., the lack of balanced-budget property. Accordingly, the proposed trading framework is described as multi-settlement and quasi-ideal. Case studies are conducted to analyze and evaluate the proposed trading framework and demonstrate the effectiveness of the proposed remedies in handling probable market deficiencies.
74 citations
TL;DR: This article addresses the optimal bidding strategy problem of a virtual power plant participating in the day-ahead (DA), real-time (RT) and spinning reserve (SR) markets (SRMs) and demonstrates the effectiveness of the proposed scheduling strategy and its operational advantages and the computational effectiveness.
Abstract: This article addresses the optimal bidding strategy problem of a virtual power plant (VPP) participating in the day-ahead (DA), real-time (RT) and spinning reserve (SR) markets (SRMs). The VPP comprises a number of dispatchable energy resources (DERs), renewable energy resources (RESs), energy storage systems (ESSs) and a number of customers with flexible demand. A two-stage risk-constrained stochastic problem is formulated for the VPP scheduling, where the uncertainty lies in the energy and reserve prices, RESs production, load consumption, as well as calls for reserve services. Based on this model, the VPP bidding/offering strategy in the DA market (DAM), RT market (RTM) and SRM is decided aiming to maximize the VPP profit considering both supply and demand-sides (DS) capability for providing reserve services. On the other hand, customers participate in demand response (DR) programs by using load curtailment (LC) and load shifting (LS) options as well as by providing reserve service to minimize their consumption costs. The proposed model is implemented on a test VPP and the optimal decisions are investigated in detail through a numerical study. Numerical simulations demonstrate the effectiveness of the proposed scheduling strategy and its operational advantages and the computational effectiveness.
65 citations
TL;DR: By clustering multiple users into independent communities based on their geographic locations, a 5G-enabled UAV-to-community offloading system is designed, able to maximize the system throughput while guaranteeing the fraction of served users.
Abstract: Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.
60 citations
TL;DR: The proposed risk-constrained offering and bidding model turns into a tri-objective optimization problem in which the normal boundary intersection (NBI) procedure is applied for its solution and demonstrates that the proposed framework is well capable of simultaneously reaching risk-taker and risk-averse strategies.
60 citations
TL;DR: A deep learning-based approach known as bi-directional long short-term memory (B-LSTM) network is employed to strengthen the VPP performance in FRM, which has strict rules with steep penalties and helps VPP operators to fulfill the day-ahead awarded bids and avoid substantial penalties in the real-time market.
58 citations
TL;DR: Based on the obtained findings on the proposed smart multi-objective framework, the IWPHEVS as a price-maker player, can manipulate locational marginal price as much as 4.4%, while the emissions can be curtailed by 40%.
50 citations
TL;DR: The results show that the two-stage optimization model presented in this paper can significantly improve the volatility of renewable energy output and the economy of the system operation under the conditions of ensuring the comprehensive benefits of virtual power plant and participating in energy market bidding efficiently.
48 citations
TL;DR: A bilevel stochastic optimization model for generating the optimal joint demand and virtual bidding strategy for a strategic retailer in the short-term electricity market, where virtual bidding is used to improve the retailer's market power in the day-ahead electricity market.
45 citations
TL;DR: Wang et al. as mentioned in this paper proposed a general framework for decentralized auctions leveraging (i) Ethereum smart contracts to trace and track bids, (ii) decentralized storage systems to upload documents related to bidding and (iii) trusted timer oracles that act as gateway between smart contract and external data feeds.
43 citations
TL;DR: In this article, a regularized variant of QRA, which utilizes the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically select the relevant regressors, is proposed.
41 citations
TL;DR: Case studies based on real-world datasets demonstrate that the proposed dispatchable region approach in MG biding can significantly improve both computation efficiency and forecasting accuracy.
Abstract: With the popularity of plug-in electric vehicles (EVs) and the development of the vehicle to grid (V2G) technology, EVs can be aggregated and behave as a controllable storage system via the Internet of Things. However, it remains an open question as to how large-scale EVs can be effectively integrated into the system-level operation. In this article, we propose a dispatchable region formation approach of EV aggregation to capture its available flexibility in microgrid (MG) bidding. The dispatchable region of EV aggregation describes the feasible operation strategy as a single entity, characterized by its power and cumulative energy limits. Instead of scheduling an individual EV, the dispatchable region of large-scale EVs enables the MG operator to directly schedule the EV aggregation toward market revenue maximization. The MG bidding strategy is formulated as a risk-constrained stochastic programming, which maximizes day-ahead market profits considering real-time imbalance settlement in a dual-pricing market. Case studies based on real-world datasets demonstrate that the proposed dispatchable region approach in MG biding can significantly improve both computation efficiency and forecasting accuracy.
TL;DR: A comparison study on how specifications are analyzed based on human cogniti... helps contractors understand the specifications properly to manage project risks.
Abstract: When bidding on construction projects, contractors need to understand the specifications properly to manage project risks. However, specifications are mainly analyzed based on human cogniti...
TL;DR: Three main technical reasons of payment reduction due to demand flexibility: load shifts, DLMP step changes, and power losses can be used as general guidelines for better decision-making for future planning and operation of demand response programs.
Abstract: Residential loads, especially heating, ventilation and air conditioners (HVACs) and electric vehicles (EVs), have great potentials to provide demand flexibility which is an attribute of grid-interactive efficient buildings (GEB). Under this new paradigm, EV and HVAC aggregator models are first developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least squares parameter estimation (DG-LSPE), which can deal with heterogeneous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). The first two levels form a bilevel model to optimize the aggregators’ payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, and the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons of payment reduction due to demand flexibility: load shifts, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.
TL;DR: A double auction-based mechanism that captures the interaction within a community energy sharing market consisting of distributed solar power prosumers and consumers and an adaptive pricing strategy is designed to assist agents better estimate the market and predict the future price.
Abstract: This article proposes a double auction-based mechanism that captures the interaction within a community energy sharing market consisting of distributed solar power prosumers and consumers. All agents are assumed to have battery energy storage systems, and can use battery for demand response. Agents can optimize the charging/discharging schedules of their battery systems for community sharing to reduce electricity costs. To determine the double-side auction market spot price, a non-cooperative game is formulated among all participants involved in the community sharing. An iterative algorithm is first designed to clear the market and mitigate the uncertainty in supply and demand. Then, an adaptive pricing strategy is designed to assist agents better estimate the market and predict the future price. A case study with 10 agents is provided to evaluate the effectiveness of the proposed community sharing market.
TL;DR: The proposed SCGTEP is tested on the 6-bus and 118-bus IEEE networks in the GAMS software and can be simultaneously improved operation and security indices about 34.5% and 100%, respectively, compared to the power flow analysis based on the optimal location of generation units and transmission lines.
TL;DR: In this article, a novel iterative uniform-price auction (IUPA) mechanism is proposed for P2P energy trading in a community microgrid, where competitive prosumers iteratively adjust their bids based on their own private information and the issued market information until reaching a state of Nash equilibrium.
TL;DR: A method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual bidding.
Abstract: This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy for awind power producer using virtual bidding, which is a kind of financial tool available in most electricity markets of the United States. In the proposed model, virtual bidding is used to improve the wind power producer's market power in the day-ahead (DA) market by trading at multiple buses, which are not limited to the locations of the wind units. The optimal joint wind power and virtual trading strategy is generated by solving a bi-level nonlinear stochastic optimization model. The upper-level problem maximizes the total expected profit of the wind power and virtual bidding while using the conditional value at risk (CVaR) for risk management. The lower-level problem represents the clearing process of the DA market. By using the Karush-Kuhn-Tucker (KKT) conditions, duality theory, and big-M method, the bi-level nonlinear stochastic model is firstly transferred into an equivalent single-level stochastic mathematical program with the equilibrium constraints (MPEC) model and then a mixed-integer linear programming (MILP) model, which can be solved by existing commercial solvers. To reduce the computational cost of solving the proposed stochastic optimization model for large systems, a method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual bidding. Case studies are performed to show the effectiveness of the proposed model and the method of reducing the number of buses considered for virtual bidding. The impacts of the transmission limits, wind unit location, risk aversion parameters, wind power volatility, and wind and virtual capacities on the price-maker trading strategy arealso studied through case studies.
27 Apr 2021
TL;DR: An optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers is devised, which establishes the customers’ responsiveness function in relation to different incentives, during which a home energy management system is introduced to implement load adjustment for electrical appliances.
Abstract: Residential customers account for an indispensable part in the demand response (DR) program for their capability to provide flexibility when the system required. However, their available DR capacity has not been fully comprehended by the aggregator, who needs the information to bid accurately on behalf of the residential customers in the market transaction. To this end, this article devised an optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers. First, we attempt to establish the customers’ responsiveness function in relation to different incentives, during which a home energy management system is introduced to implement load adjustment for electrical appliances. Second, the functional relation is applied to the aggregator's decision-making process to formulate the optimal bidding strategy in the day-ahead market and the optimal scheduling scheme for the energy storage system with the aim to maximize its own revenue. Finally, the validity of the proposed method is verified using the dataset from the Pecan Street experiment in Austin. The obtained outcome demonstrates the practical rationality of the proposed method.
TL;DR: A bi-level stochastic programming is proposed for the integrated heat-energy and reserve scheduling of the smart MGs in presence of energy storage system (ESS) and demand response (DR) programs based on the maximization of total social welfare as objective function.
TL;DR: A network-secure bidding optimization strategy to assist aggregators of multi- energy systems calculating electricity (energy and reserve), gas and carbon bids, considering multi-energy network constraints is presented, a distributed approach based on the alternating direction method of multipliers.
TL;DR: A new framework for the optimal bidding strategy of a Technical Virtual Power Plant (TVPP) in different markets is proposed and the results confirms that the local market creates an opportunity for CHP owner, TVPP and the heat consumers to make more profit as well as achieving better technical performance for distribution energy systems.
TL;DR: Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep $Q$ network and deep deterministic policy gradient (DDPG) demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.
Abstract: In this paper, a day-ahead electricity market bidding problem with multiple strategic generation company (GEN-CO) bidders is studied. The problem is formulated as a Markov game model, where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies. Considering unobservable information in the problem, a model-free and data-driven approach, known as multi-agent deep deterministic policy gradient (MADDPG), is applied for approximating the Nash equilibrium (NE) in the above Markov game. The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks. The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case. Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep $Q$ network and deep deterministic policy gradient (DDPG) demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains. In addition, the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency, which is feasible for real-world applications.
TL;DR: A tri-layer hierarchical framework is developed to formulate the bidding plans to the superordinate market operator and the dynamic price incentive curves for the subordinate DER owners cooperatively by using the discrete price quota curves, big-M method, KKT optimal conditions, and strong duality theorem.
Abstract: This article proposes a coordinated operation strategy for a virtual power plant (VPP) with multiple DER aggregators under the wholesale energy and regulation service market. Considering the market clearing process, VPP operation profit, and DER aggregators’ interests, a tri-layer hierarchical framework for VPP is developed to formulate the bidding plans to the superordinate market operator and the dynamic price incentive curves for the subordinate DER owners cooperatively. By using the discrete price quota curves, big-M method, KKT optimal conditions, and strong duality theorem, the tri-layer problem is transformed into an equivalent and tractable mixed integer linear programming problem. Case study with the 141-bus system is conducted to validate the effectiveness of the proposed approach. The advantages of lower cost with operation security guarantee and higher flexibility are demonstrated by comparing with other models.
TL;DR: A stochastic decision-making framework is presented in which a wind power producer provides some required reserve capacity from demand response aggregators (DRAs) in a peer-to-peer (P2P) structure in which the upper level maximizes the WPP's profit based on the optimal bidding in the day-ahead and balancing markets, whereas the lower level minimizes DRAs' costs.
Abstract: In this article, a stochastic decision-making framework is presented in which a wind power producer (WPP) provides some required reserve capacity from demand response aggregators (DRAs) in a peer-to-peer (P2P) structure. In this structure, each DRA is able to choose the most competitive WPP, and purchase energy and sell reserve capacity to that WPP under a bilateral contract-based P2P electricity trading mechanism. Based on this structure, the WPP can determine the optimal buying reserve from DRAs to offset part of wind power deviation. The proposed framework is formulated as a bilevel stochastic model in which the upper level maximizes the WPP's profit based on the optimal bidding in the day-ahead and balancing markets, whereas the lower level minimizes DRAs' costs. In order to incorporate the risk associated with the WPP's decisions and to assess the effect of scheduling reserves on the profit variability, conditional value at risk is employed.
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TL;DR: The empirical results validate the theoretical findings and show that both the welfare and revenue can be improved by selecting the weight of the boosts properly.
Abstract: Auto-bidding has become one of the main options for bidding in online advertisements, in which advertisers only need to specify high-level objectives and leave the complex task of bidding to auto-bidders. In this paper, we propose a family of auctions with boosts to improve welfare in auto-bidding environments with both return on ad spend constraints and budget constraints. Our empirical results validate our theoretical findings and show that both the welfare and revenue can be improved by selecting the weight of the boosts properly.
TL;DR: A distributed smart contract solution based on a stochastic bidding process, which helps CEVs to sell and buy electricity with their maximum profitability in parking lots based on consortium blockchain, machine learning, and Game theoretic model is proposed.
Abstract: Connected electric vehicles (CEVs) can help cities to reduce road congestion and increase road safety. With the technical improvement made to the battery system in terms of capacity and flexibility, CEVs, as mobile power plants can be an important actor for the electricity markets. Especially, they can trade electricity between each other when supply stations are full or temporarily not available. In this article, we propose an advanced decentralized electricity trading framework between CEVs in parking lots based on consortium blockchain, machine learning, and Game theoretic model. We design a distributed smart contract solution based on a stochastic bidding process, which helps CEVs to sell and buy electricity with their maximum profitability. Finally, numerical simulations with MATLAB and Solidity are conducted to prove the effectiveness of our proposed solution. Also, a comparison with another method in terms of CEVs’ profitability improvement and energy trading management is provided.
TL;DR: In this paper, the optimal stochastic bidding strategy in joint energy and AS (regulation up and regulation down, spinning reserve and non-spinning reserve) market is modeled.
TL;DR: In this article, a large-scale cross-sectoral capacity expansion and long-term cross-border transmission expansion study for the future integrated European system was conducted, and it was shown that bi-and multivalent cross-sectorsal electricity consumers will be making market bids based on their valid opportunity costs.
TL;DR: This work model the sequential interactions between the CI environment and a test case prioritization agent as an RL problem, using three alternative ranking models, and shows that the best RL solutions provide a significant accuracy improvement over previous RL-based work, with prioritization strategies getting close to being optimal.
Abstract: Continuous Integration (CI) significantly reduces integration problems, speeds up development time, and shortens release time. However, it also introduces new challenges for quality assurance activities, including regression testing, which is the focus of this work. Though various approaches for test case prioritization have shown to be very promising in the context of regression testing, specific techniques must be designed to deal with the dynamic nature and timing constraints of CI. Recently, Reinforcement Learning (RL) has shown great potential in various challenging scenarios that require continuous adaptation, such as game playing, real-time ads bidding, and recommender systems. Inspired by this line of work and building on initial efforts in supporting test case prioritization with RL techniques, we perform here a comprehensive investigation of RL-based test case prioritization in a CI context. To this end, taking test case prioritization as a ranking problem, we model the sequential interactions between the CI environment and a test case prioritization agent as an RL problem, using three alternative ranking models. We then rely on carefully selected and tailored state-of-the-art RL techniques to automatically and continuously learn a test case prioritization strategy, whose objective is to be as close as possible to the optimal one. Our extensive experimental analysis shows that the best RL solutions provide a significant accuracy improvement over previous RL-based work, with prioritization strategies getting close to being optimal, thus paving the way for using RL to prioritize test cases in a CI context.
TL;DR: A novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper and the presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.
Abstract: As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.