Showing papers on "Bidding published in 2019"
TL;DR: A new mathematical model as a hybrid robust-stochastic method is proposed in order to maximize the expected profit of a compressed air energy system and formulates mixed-integer linear programming and obtains optimal offering and bidding curves.
229 citations
TL;DR: This work focuses on the trading between the cloud/fog computing service provider and miners, and proposes an auction-based market model for efficient computing resource allocation, and designs an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency.
Abstract: As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of “block mining” by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog computing servers. Therefore, we focus on the trading between the cloud/fog computing service provider and miners, and propose an auction-based market model for efficient computing resource allocation. In particular, we consider a proof-of-work based blockchain network, which is constrained by the computing resource and deployed as an infrastructure for decentralized data management applications. Due to the competition among miners in the blockchain network, the allocative externalities are particularly taken into account when designing the auction mechanisms. Specifically, we consider two bidding schemes: the constant-demand scheme where each miner bids for a fixed quantity of resources, and the multi-demand scheme where the miners can submit their preferable demands and bids. For the constant-demand bidding scheme, we propose an auction mechanism that achieves optimal social welfare. In the multi-demand bidding scheme, the social welfare maximization problem is NP-hard. Therefore, we design an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we show that our proposed auction mechanisms with the two bidding schemes can efficiently maximize the social welfare of the blockchain network and provide effective strategies for the cloud/fog computing service provider.
170 citations
TL;DR: Numerical results show that the method is efficient in finding the bidding curves in the day-ahead market through the optimal management of flexibility requests sent to clusters, as well as of DER in LES and interactions among LES.
Abstract: The penetration of distributed energy resources (DER), including distributed generators, storage devices, and demand response (DR) is growing worldwide, encouraged by environmental policies and decreasing costs. To enable DER local integration, new energy players as aggregators appeared in the electricity markets. This player, acting toward the grid as one entity, can offer new services to the electricity market and the system operator by aggregating flexible DER involving both DR and generation resources. In this paper, an optimization model is provided for participation of a DER aggregator in the day-ahead market in the presence of demand flexibility. This player behaves as an energy aggregator, which manages energy and financial interactions between the market and DER organized in local energy systems (LES), which are in charge to satisfy the multienergy demand of a set of building clusters with flexible demand. A stochastic mixed-integer linear programming problem is formulated by considering uncertainties of intermittent DER facilities and day-ahead market price, to find the optimal bidding strategies while maximizing the expected aggregator's profit. Numerical results show that the method is efficient in finding the bidding curves in the day-ahead market through the optimal management of flexibility requests sent to clusters, as well as of DER in LES and interactions among LES.
158 citations
Posted Content•
TL;DR: This work highlights the large, complex risks created by transaction-ordering dependencies in smart contracts and the ways in which traditional forms of financial-market exploitation are adapting to and penetrating blockchain economies.
Abstract: Blockchains, and specifically smart contracts, have promised to create fair and transparent trading ecosystems.
Unfortunately, we show that this promise has not been met. We document and quantify the widespread and rising deployment of arbitrage bots in blockchain systems, specifically in decentralized exchanges (or "DEXes"). Like high-frequency traders on Wall Street, these bots exploit inefficiencies in DEXes, paying high transaction fees and optimizing network latency to frontrun, i.e., anticipate and exploit, ordinary users' DEX trades.
We study the breadth of DEX arbitrage bots in a subset of transactions that yield quantifiable revenue to these bots. We also study bots' profit-making strategies, with a focus on blockchain-specific elements. We observe bots engage in what we call priority gas auctions (PGAs), competitively bidding up transaction fees in order to obtain priority ordering, i.e., early block position and execution, for their transactions. PGAs present an interesting and complex new continuous-time, partial-information, game-theoretic model that we formalize and study. We release an interactive web portal, this http URL, to provide the community with real-time data on PGAs.
We additionally show that high fees paid for priority transaction ordering poses a systemic risk to consensus-layer security. We explain that such fees are just one form of a general phenomenon in DEXes and beyond---what we call miner extractable value (MEV)---that poses concrete, measurable, consensus-layer security risks. We show empirically that MEV poses a realistic threat to Ethereum today.
Our work highlights the large, complex risks created by transaction-ordering dependencies in smart contracts and the ways in which traditional forms of financial-market exploitation are adapting to and penetrating blockchain economies.
150 citations
TL;DR: A two-stage stochastic optimization model to support an aggregator of prosumers in the definition of bids for the day-ahead energy and secondary reserve markets shows that the proposed bidding strategy reduces the costs of both aggregator and prosumers by 40% compared to a bidding strategy typically used by retailers.
146 citations
TL;DR: A linear model with integer variables is developed to derive offering and bidding curves, which are robust against the uncertainty associated with the load demand of the large consumer, and results show that the higher amount of uncertain parameter is considered, the higher procurement price has resulted for theLarge consumer.
143 citations
TL;DR: A novel prediction-integration strategy optimization (PISO) model is proposed, which learns the interaction relationship between prosumer bidding actions and market responses from historical transaction data, and can be conveniently transformed and integrated into the prosumer operation optimization model in the form of constraints.
134 citations
TL;DR: In this paper, a comparative analysis of auction mechanisms and bidding strategies for P2P solar electricity exchanges in terms of market demand and supply metrics is presented, and the impacts of different bidding strategies, including game theoretic approaches, on the economic efficiencies of the P2PC transactive energy market are also studied.
103 citations
TL;DR: The proposed distributionally robust scheduling model maximizes the base-case system social welfare plus the worst-case expected load shedding cost, and is cast into a mixed-integer linear programming problem to enhance computational tractability.
Abstract: This paper proposes a distributionally robust scheduling model for the integrated gas-electricity system (IGES) with electricity and gas load uncertainties, and further studies the impact of integrated gas-electricity demand response (DR) on energy market clearing, as well as locational marginal electricity and gas prices (LMEPs and LMGPs). The proposed model maximizes the base-case system social welfare (i.e., revenue from price-sensitive DR loads minus energy production cost) minus the worst-case expected load shedding cost. Price-based gas-electricity DRs are formulated via price-sensitive demand bidding curves while considering DR participation levels and energy curtailment limits. By linearizing nonlinear Weymouth gas flow equations via Taylor series expansion and further approximating recourse decisions as affine functions of uncertainty parameters, the formulation is cast into a mixed-integer linear programming problem to enhance computational tractability. Case studies illustrate effectiveness of the proposed model for ensuring system security against uncertainties, avoiding potential transmission congestions, and increasing financial stability of DR providers.
102 citations
TL;DR: The proposed hybrid forecasting framework is validated in the New South Wales electricity market, which demonstrates that the developed approach is truly better than the benchmark models used and a reliable and promising tool for short-term electricity price forecasting.
101 citations
TL;DR: In this paper, peers encrypt their private data before storing it on the chain and use secure MPC whenever such private data are needed in a transaction on Hyperledger Fabric.
Abstract: Hyperledger Fabric is a “permissioned” blockchain architecture, providing a consistent distributed ledger, shared by a set of “peers” that must all have the same view of its state. For many applications, it is desirable to keep private data on the ledger, but the same-view principle makes it challenging to implement. In this paper, we explore supporting private data on Fabric using secure multiparty computation (MPC). In our solution, peers encrypt their private data before storing it on the chain and use secure MPC whenever such private data are needed in a transaction. We created a demo of our solution, implementing a bidding system where sellers list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price. We implemented a smart contract that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. We identified two basic services that should be added to Hyperledger Fabric to support our solution, inspiring follow-up work to implement and add these services to the Hyperledger Fabric architecture.
TL;DR: A two-stage stochastic optimization model is proposed to exploit the load and generation flexibility of the prosumers to minimize the net cost of the aggregator buying and selling energy in the day-ahead and real-time markets, as well as to maximize the revenue of selling tertiary reserve during the real- time stage.
Abstract: This paper addresses the participation of an aggregator of small prosumers in the energy and tertiary reserve markets. A two-stage stochastic optimization model is proposed to exploit the load and generation flexibility of the prosumers. The aim is to define energy and tertiary reserve bids to minimize the net cost of the aggregator buying and selling energy in the day-ahead and real-time markets, as well as to maximize the revenue of selling tertiary reserve during the real-time stage. Scenario-based stochastic programming is used to deal with the uncertainties of photovoltaic power generation, electricity demand, outdoor temperature, end-users’ behavior, and preferences. A case study of 1000 small prosumers from MIBEL is used to compare the proposed strategy to two other strategies. The numerical results show that the proposed strategy reduces the bidding net cost of the aggregator by 48% when compared to an inflexible strategy typically used by retailers.
TL;DR: In this paper, a joint bidding and pricing problem is formulated as a Markov decision process (MDP) with continuous state and action spaces in which the energy bid and the energy price are two actions that share a common objective.
Abstract: In this paper, we address the problem of jointly determining the energy bid submitted to the wholesale electricity market (WEM) and the energy price charged in the retailed electricity market (REM) for a load serving entity (LSE). The joint bidding and pricing problem is formulated as a Markov decision process (MDP) with continuous state and action spaces in which the energy bid and the energy price are two actions that share a common objective. We apply the deep deterministic policy gradient (DDPG) algorithm to solve this MDP for the optimal bidding and pricing policies. Yet, the DDPG algorithm typically requires a significant number of state transition samples, which are costly in this application. To this end, we apply neural networks to learn dynamical bid and price response functions from historical data to model the WEM and the collective behavior of the end use customers (EUCs), respectively. These response functions explicitly capture the inter-temporal correlations of the WEM clearing results and the EUC responses and can be utilized to generate state transition samples without any cost. More importantly, the response functions also inform the choice of states in the MDP formulation. Numerical simulations illustrated the effectiveness of the proposed methodology.
TL;DR: A novel scheme for optimizing the operation and bidding strategy of VPPs and the results verify the effectiveness of the proposed method VPP with various combinations of renewable energy sources, energy storage systems, and loads.
Abstract: As an aggregator involved in various renewable energy sources, energy storage systems, and loads, a virtual power plant (VPP) plays a key role as a prosumer. A VPP may enable itself to supply energy and ancillary services to the utility grid. This paper proposes a novel scheme for optimizing the operation and bidding strategy of VPPs. By scheduling the energy storage systems, demand response, and renewable energy sources, VPPs can join bidding markets to achieve maximum benefits. The potential uncertainties caused by renewable energy sources and the demand response are considered in a robust optimization model. Moreover, the robust VPP optimization accounts for its influence on markets to ensure optimal energy and reserve capacity bidding transactions in the day-ahead market and deals balancing in the real-time market. To demonstrate the performance of the proposed scheme, markets comprising various participants and managed by the system operator are implemented using mathematical models. The proposed method is evaluated using an illustrative system and the practical Taiwan power (Taipower) system with diverse uncertainty levels. The numerical results demonstrate the promising performance and the efficiency of the proposed method. The results also verify the effectiveness of the proposed method VPP with various combinations of renewable energy sources, energy storage systems, and loads.
TL;DR: In this article, a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM) was proposed, which outperformed the compared forecasting methods in terms of accuracy.
Abstract: This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.
TL;DR: The capability of the proposed methodology for probabilistic energy price forecast based on Bayesian deep learning techniques to achieve robust performances in out-of-sample conditions while providing forecast uncertainty indications is demonstrated.
TL;DR: In this paper, a coordinated retail market framework is proposed to achieve the coordinated clearing of electric and heat load, managing the district energy generation and consumption through transactive control methods, and the market clearing rules are defined with the aim of maximizing the net revenue of integrated energy service agency to realize the optimal energy allocation of energy station devices.
TL;DR: This work studies how budget-constrained advertisers may co-fund ad placements through bidding in repeated auctions based on realized viewer information in online advertising markets.
Abstract: In online advertising markets, advertisers often purchase ad placements through bidding in repeated auctions based on realized viewer information. We study how budget-constrained advertisers may co...
TL;DR: Simulation results show that the proposed bi-level multi-time scale scheduling method can realize the optimal distribution of operators’ power generation and form the internal electricity price that reflects the internal supply and demand level of virtual power plant.
TL;DR: In this paper, the design variables that affect DER access to and participation in the organized balancing market include different features of auction configuration as well as a number of formal, administrative and technical aspects of market design.
TL;DR: The results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model and has a positive effect on the system’s total expected profit.
TL;DR: It is concluded that the stochastic method is efficient for optimal-bidding of GenCos owning CAES and wind units and also for risk-averse GenCos.
TL;DR: A mathematically proven practical approach for bidding of an autonomous smart transactive agent in local energy markets by modeling behaviors of both risk-neutral and risk-averse agents selling energy to the market taking into account expected profit and risk criteria.
Abstract: Expanding the electricity market into the retail domain calls for inexpensive mass-produced smart devices that enable enable small customers to customers to participate in local energy transactions by managing the energy production/consumption and submitting buy/sell bids to the market. In this context, this paper presents a mathematically proven practical approach for bidding of an autonomous smart transactive agent in local energy markets. To reach this goal, behaviors of both risk-neutral and risk-averse agents selling energy to the market are modeled taking into account expected profit and risk criteria. Subsequently, an optimal multi-step quantity-price bidding strategies of risk-neutral and risk-averse agents are extracted. In this context, this paper: 1) introduces the effective metrics and criteria for evaluating a bidding strategy; 2) provides all theorems and lemmas required for reaching an optimal bidding strategy for either a risk-neutral or risk-averse agent; and 3) evaluates and presents the developed approach for different market environments. The developed methodology is shown to be effective in practical applications especially for local markets.
TL;DR: A novel discrete-timely double-sided auction model that facilitates energy trading between prosumers in near real-time and forward markets is described and can increase self-sufficiency and self-consumption of a microgrid while reducing the prosumer costs on average by 23%.
19 Aug 2019
TL;DR: In this article, the authors proposed an optimal bidding strategy model for the load aggregator (LA) that implements the demand response program (DRP), which enables the LA to reduce the risk of financial loss caused by price volatility.
Abstract: In a typical electricity market, the load aggregator (LA) bids in the wholesale market to purchase electricity and meet the expected demand of its customers in the retail market. However, considering the uncertainty of the wholesale market prices, the LA has to undertake all the risks arising from the price volatility in the wholesale market, which may make the LA suffer from financial loss under some scenarios such as price spikes. To this end, first, this article proposes an optimal bidding strategy model for the LA that implements the demand response program (DRP), which enables the LA to reduce the risk of financial loss caused by price volatility. The bidding model is a mixed integer linear programming problem, which can be solved efficiently via a commercial solver. Second, making a rational and quantitative compensation mechanism is significant for the LA to induce its customers to participate in the DRP while there are few studies investigating it, hence, this article designs a quantitative compensation mechanism for the LA. Case studies using a dataset from the Thames valley vision verify the effectiveness of the proposed bidding model. The results confirm that the implementation of DRP not only brings great profits to LA but also benefits the other entities in the electricity market.
TL;DR: A novel multi- agent deep reinforcement learning (MA-DRL) based methodology, combining multi-agent intelligence, the deep policy gradient (DPG) method, and an innovative long short term memory (LSTM) based representation network for optimizing the offering strategies of multiple self-interested generation companies (GENCOs) as well as exploring the market outcome stemming from their interactions.
Abstract: Previously works on analysing imperfect electricity markets have employed conventional game-theoretic approaches. However, such approaches necessitate that each strategic market player has full knowledge of the operating parameters and the strategies of its rivals as well as the computational algorithm of the market clearing process. This unrealistic assumption, along with the modeling and computational complexities, renders such approaches less applicable for conducting practical multi-period and multi-spatial equilibrium analysis. This paper proposes a novel multi-agent deep reinforcement learning (MA-DRL) based methodology, combining multi-agent intelligence, the deep policy gradient (DPG) method, and an innovative long short term memory (LSTM) based representation network for optimizing the offering strategies of multiple self-interested generation companies (GENCOs) as well as exploring the market outcome stemming from their interactions. The proposed approach is tailored to align with the nature of the examined problem by posing it, for the first time, in multi-dimensional continuous state and action spaces, enabling GENCOs to receive accurate feedback regarding the impact of their offering strategies on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, and thereby facilitates more accurate equilibrium analysis. The proposed LSTM-based representation network extracts discriminative features which further improves the learning performance and thus promises more profitable offerings strategies for each GENCO. Case studies demonstrate that the proposed method i) achieves a significantly higher profit than state-of-the-art RL methods for a single GENCO's optimal offering strategy problem and ii) outperforms the state-of-the-art equilibrium programming models in efficiently identifying an imperfect market equilibrium with/without network congestion. Quantitative economic analysis is carried out on the obtained equilibrium.
TL;DR: A hybrid market framework is proposed which integrates pool emergency transactions and bilateral contracts in order to reduce the system risk in face of different contingency events in renewable-based multi-microgrid systems.
Abstract: In this paper, a new market mechanism is proposed to quantify the value of emergency energy transactions in renewable-based multi-microgrid (MMG) systems. To reach this goal, main requirements and features of such emergency market are identified. Subsequently, a hybrid market framework is proposed which integrates pool emergency transactions and bilateral contracts in order to reduce the system risk in face of different contingency events. For settling different transactions in this market, the main procedure which should be followed by distribution system operator to properly address bidding of microgrids (MGs) as well as system technical constraints is introduced. In addition, a simple and efficient method is introduced to determine MGs’ purchase/sell bids considering available resources and flexible demands. Market settling process and MGs bidding procedure are represented using efficient optimization models and effectiveness of the proposed framework is demonstrated via implementation on a test MMG system.
TL;DR: A cluster-based optimization approach to support an aggregator in the definition of demand and supply bids for the day-ahead energy market, which reduces effectively the execution times of the bidding optimization problems, without affecting the quality of the energy bids.
TL;DR: The proposed robust bidding strategies and scheduling of a price-maker MGA are obtained considering a hypothetical test system and the results show that the robust scheduling and also the market prices are completely changed for different strategies of the MGA.
Abstract: This study presents a model for the activities of the price-maker microgrid aggregator (MGA). In this model, an MGA is considered to aggregate several microgrids (MGs) and be in charge of obtaining an optimal bidding strategy for MGs as well as scheduling their resources and demand. Two price-maker strategies (the marginal and non-marginal strategies for players) are proposed and the robust scheduling and optimal transactions of a price-taker MGA are also obtained in order to analyse different bidding behaviour of MGA. A robust optimisation is used in this model in order to capture uncertainties associated with renewable generation in the worst-case situation. Accordingly, the robust solution is obtained for the optimal scheduling of an MGA participating in the pool-based day-ahead electricity market. The proposed robust bidding strategies and scheduling of a price-maker MGA are obtained considering a hypothetical test system and the results are compared with the bidding strategy and robust scheduling of a price-taker MGA. The results show that the robust scheduling and also the market prices are completely changed for different strategies of the MGA. Also, using the proposed model for the price-maker MGA increases the profits of MGs.
TL;DR: An energy sharing scheme that allows users to share DERs with neighbors, and a novel incentive mechanism for benefit allocation without users’ bidding on electricity prices is developed.
Abstract: To improve the controllability and utilization of distributed energy resources (DERs), distribution-level electricity markets based on consumers’ bids and offers have been proposed. However, the transaction costs will dramatically increase with the rapid development of DERs. Therefore, in this paper, we develop an energy sharing scheme that allows users to share DERs with neighbors, and design a novel incentive mechanism for benefit allocation without users’ bidding on electricity prices. In the energy sharing scheme, an aggregator organizes a number of electricity users, and trades with the connected power grid. The aggregator is aimed at minimizing the total costs by matching the surplus energy from DERs and electrical loads. A novel index, termed as sharing contribution rate (SCR), is presented to evaluate different users’ contributions to the energy sharing. Then, based on users’ SCRs, an efficient benefit allocation mechanism is implemented to determine the aggregator’s payments to users that incentivize their participation in energy sharing. To avoid users’ bidding, we propose a decentralized framework for the energy sharing and incentive mechanism. Case studies based on real-world datasets demonstrate that the aggregator and users can benefit from the energy sharing scheme, and the incentive mechanism allocates the benefits according to users’ contributions.