Showing papers on "Bidding published in 2020"

An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles

Abstract: In a near future, electric vehicles (EVs) will constitute considerable part of transportation systems due to their important aspects such as being environment friendly. To manage high number of EVs, developing hydrogen storage-based intelligent parking lots (IPLs) can help power system operators to overcome caused problems by high penetration of EVs. In this work, a new method is applied to get optimal management of IPLs in an uncertain environment and provide optimal bidding curves to take part in power market. The main purpose of this work is to get optimal bidding curves with considering power price uncertainty and optimal operation of IPLs. To model uncertainty of power price in the power market and develop optimal bidding curve, the opportunity, deterministic and robustness functions of the information gap decision theory (IGDT) technique has been developed. Obtained results has been presented in three strategies namely risk-taker, risk-neutral, and risk-averse corresponding to opportunity, deterministic, and robustness functions of the IGDT technique. In order to demonstrate the effects of demand response program (DRP), each strategy is optimized with and without DRP cases. The mixed-integer non-linear programming model is used to formulate the proposed problem which is solved using the GAMS optimization software under DICOPT solver.

Flash Boys 2.0: Frontrunning in Decentralized Exchanges, Miner Extractable Value, and Consensus Instability

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, frontrun.me, 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.

Deep Reinforcement Learning for Strategic Bidding in Electricity Markets

Abstract: Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants’ physical non-convex operating characteristics, while conventional RL methods require discretization of state and/or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy.

A hierarchical energy management system for multiple home energy hubs in neighborhood grids

Abstract: This paper presents a hierarchical energy management system (HEMS) for multiple home energy hubs in the neighborhood grid (MHEHNG). The main objectives are maximizing financial profit and shaving the peak of upstream grid. This way, the proposed HEMS manages the energy generation and energy storing, as well as energy purchase/sale of each home energy hub (HEH) under the two levels including lower and upper levels. The lower level is responsible for supplying the internal load and reducing the energy cost in each HEH. The upper level is the central energy management system (CEMS) which is focused on forming a coalition between the local HEHs, as well as giving the tempting offers to increase the financial profit through a heuristic bidding strategy. The principle of proposed bidding strategy is based on weighted distributing of excess power among consumers that is one of the contribution of this paper. It leads to trading more energy at the lowest possible price. Determining the most appropriate operational scenario in each HEH requires the investigation of both technical and financial aspects. A novel scenario selector method has been proposed based on SOC-tariff plane. This is another contribution of this work. A simulator has been implemented in the MATLAB/GUI software environment to facilitate the evaluation of proposed HEMS performance. The simulation results indicate the effectiveness of the proposed HEMS. They show a decrease in the total energy cost of the CBs by almost 9.4%, and an increase in the total profit of the HEHs by 4.55%. Also, it was found out that the energy can be purchased from HEHs at varying rates and can be sold to the consumers at almost constant rates by using the proposed bidding strategy. This motivates HEHs to submit more power at lower tariffs to the CEMS.

Intraday Residential Demand Response Scheme Based on Peer-to-Peer Energy Trading

Abstract: The intermittency introduced by the increasing integration of distributed renewable energy sources is challenging the efficient operation of residential distribution systems. A promising solution to tackle this challenge is the implementation of residential demand response through responsive household appliances such as heat pumps, refrigeration devices, and energy storage units. In this article, a peer-to-peer energy trading platform among residential houses is proposed to coordinate demand response schemes and level off potential generation/consumption disturbances in the hour-ahead intraday context. First, the day-ahead and intraday energy management models for residential houses are established considering the characteristics of responsive household appliances and energy storages. The discomfort and possible economic losses for performing demand responses are quantified with respect to the risk preferences of residential customers. The peer-to-peer energy trading platform is developed and a double-auction mechanism employed to promote the collaborative demand response schemes in the face of disturbances. An optimal bidding strategy of residential houses is also proposed. The feasibility of the proposed models and bidding strategy are verified through case studies. It is also illustrated that the residential demand response schemes and intraday peer-to-peer energy trading are effective in managing the uncertainties of load demand and renewable generation.

Coordinated Bidding Strategy of Wind Farms and Power-to-Gas Facilities Using a Cooperative Game Approach

Abstract: Power-to-gas (P2G) technology is an effective way to provide additional flexibility to electricity systems with high penetration of renewable energy. Effective P2G bidding strategies in electricity markets can bring in arbitrage opportunities. This article proposes a coordinated bidding strategy for wind farms and P2G facilities using a cooperative game approach. A coordination framework for optimizing the profit of wind farms and P2G facilities is presented, in which wind power participates in day-ahead, real-time and reserve markets, and the P2G facilities participate in day-ahead and reserve markets for arbitrage. Wind farms strategically purchase reserve to avoid high imbalance penalties. As a coalition, wind farms can provide energy to P2G facilities and receive reserve capacity in return. The coordinated bidding strategy is modeled on stochastic programming to maximize the expected joint profit of wind farms and P2G facilities. Nucleolus and Shapley-value based methods are proposed to allocate profits to each player in the coalition. Numerical results based on real world market data demonstrate that the proposed bidding strategy can increase the profits of both wind farms and P2G facilities. Strategically purchasing reserve can reduce the imbalance penalties of wind farms, and participating in reserve markets of P2G facilities can increase profit.

Optimal Participation of Residential Aggregators in Energy and Local Flexibility Markets

Abstract: This paper presents an optimization model for Home Energy Management Systems from an aggregator’s standpoint. The aggregator manages a set of resources such as PV, electrochemical batteries and thermal energy storage by means of electric water heaters. Resources are managed in order to participate in the day-ahead energy and local flexibility markets, also considering grid constraint support at the Point of Common Coupling. The resulting model is a Mixed-Integer Linear Programming problem in which the objective is to minimize day-ahead operation costs for the aggregator while complying with energy commitments in the day-ahead market and local flexibility requests. Three sources of uncertainty are considered: energy prices, PV production and load. Adjustable Robust Optimization is used to find a robust counterpart of the problem for including uncertainty. The results obtained show that using robust optimization allows strategic bidding to capture uncertainties while complying with obligations in the wholesale and local market. Data from a real-life energy community with 25 households is used to validate the proposed robust bidding methodology.

Strategic Bidding of Hybrid AC/DC Microgrid Embedded Energy Hubs: A Two-Stage Chance Constrained Stochastic Programming Approach

Abstract: To realize high efficient energy conversion among multiple energy carriers in market environment, the hybrid ac/dc microgrid is embedded as the electrical hub for energy hubs (EHs), and a stochastic day-ahead bidding strategy is proposed for the energy hub operators (EHOs). The electricity, heating, and cooling are managed to balance the operational cost and thermal service quality, while participating into the day-ahead market and real-time market. The uncertainties of prices, electrical loads, and ambient temperature are depicted by scenario trees, and are managed by a two-stage stochastic optimization scheme to minimize the expected and conditional value at the risk of operational cost. This stochastic optimization problem is reformulated to a linear programming (LP) problem under given conditions. In addition, a chance constraint is proposed to relax the quality of thermal services, and a two-stage chance constrained stochastic programming is formulated accordingly. It is further reformulated to a mixed integer LP problem. Simulations have been carried out on an EH with multiple types of energy generation, conversion, and storage systems. Results have verified the effectiveness of the proposed method, providing efficient bidding curves to the EHO through the stochastic management. Sensitive analysis shows that the proposed strategy can balance the operational cost and service quality via the adjustment of chance constraints.

Peer-to-Peer Energy Trading in Virtual Power Plant Based on Blockchain Smart Contracts

Abstract: A novel Peer-to-peer (P2P) energy trading scheme for a Virtual Power Plant (VPP) is proposed by using Smart Contracts on Ethereum Blockchain Platform. The P2P energy trading is the recent trend the power society is keen to adopt carrying out several trial projects as it eases to generate and share the renewable energy sources in a distributed manner inside local community. Blockchain and smart contracts are the up-and-coming phenomena in the scene of the information technology used to be considered as the cutting-edge research topics in power systems. Earlier works on P2P energy trading including and excluding blockchain technology were focused mainly on the optimization algorithm, Information and Communication Technology, and Internet of Things. Therefore, the financial aspects of P2P trading in a VPP framework is focused and in that regard a P2P energy trading mechanism and bidding platform are developed. The proposed scheme is based on public blockchain network and auction is operated by smart contract addressing both cost and security concerns. The smart contract implementation and execution in a VPP framework including bidding, withdrawal, and control modules developments are the salient feature of this work. The proposed architecture is validated using realistic data with the Ethereum Virtual Machine (EVM) environment of Ropsten Test Network.

The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems.

Abstract: Online allocation problems with resource constraints are central problems in revenue management and online advertising. In these problems, requests arrive sequentially during a finite horizon and, for each request, a decision maker needs to choose an action that consumes a certain amount of resources and generates reward. The objective is to maximize cumulative rewards subject to a constraint on the total consumption of resources. In this paper, we consider a data-driven setting in which the reward and resource consumption of each request are generated using an input model that is unknown to the decision maker. We design a general class of algorithms that attain good performance in various inputs models without knowing which type of input they are facing. In particular, our algorithms are asymptotically optimal under stochastic i.i.d. input model as well as various non-stationary stochastic input models, and they attain an asymptotically optimal fixed competitive ratio when the input is adversarial. Our algorithms operate in the Lagrangian dual space: they maintain a dual multiplier for each resource that is updated using online mirror descent. By choosing the reference function accordingly, we recover dual sub-gradient descent and dual exponential weights algorithm. The resulting algorithms are simple, fast, and have minimal requirements on the reward functions, consumption functions and the action space, in contrast to existing methods for online allocation problems. We discuss applications to network revenue management, online bidding in repeated auctions with budget constraints, online proportional matching with high entropy, and personalized assortment optimization with limited inventories.

Agent-Based Modeling in Electricity Market Using Deep Deterministic Policy Gradient Algorithm

Abstract: Game theoretic methods and simulations based on reinforcement learning (RL) are often used to analyze electricity market equilibrium. However, the former is limited to a simple market environment with complete information, and difficult to visually reflect the tacit collusion; while the conventional RL algorithm is limited to low-dimensional discrete state and action spaces, and the convergence is unstable. To address the aforementioned problems, this paper adopts deep deterministic policy gradient (DDPG) algorithm to model the bidding strategies of generation companies (GenCos). Simulation experiments, including different settings of GenCo, load and network, demonstrate that the proposed method is more accurate than conventional RL algorithm, and can converge to the Nash equilibrium of complete information even in the incomplete information environment. Moreover, the proposed method can intuitively reflect the different tacit collusion level by quantitatively adjusting GenCos’ patience parameter, which can be an effective means to analyze market strategies.

Towards Differential Privacy-Based Online Double Auction for Smart Grid

Abstract: In this paper, to address the issue of demand response in the smart grid with island MicroGrids (MGs), we introduce an effective and secure auction market that allows electric vehicles (EVs) having surplus energy to act as sellers, and the EVs having insufficient energy in the island MGs to act as buyers. There are two primary challenges in designing an effective auction market in the smart grid. First, the auction market scheme shall be online, allowing buyers and sellers to enter the market at any time, and satisfy several critical economic properties (individual rationality, incentive compatibility, and so on.). Second, the sensitive information of participants shall be protected in the auction process. To address these challenges, we present a novel privacy-preserving online double auction scheme based on differential privacy. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, aiming at solving the social welfare maximization problem to match buyers and sellers. The principle of differential privacy is leveraged to protect the privacy of EVs’ sensitive bidding information. Via theoretical analysis, we demonstrate that our designed auction scheme satisfies both economic and privacy-preserving properties, including individual rationality, incentive compatibility, weak budget balance, and $\varepsilon $ -differential privacy. We conduct an extensive performance evaluation to measure the effectiveness of our proposed scheme. Our experimental results show that the proposed auction scheme can not only ensure the privacy of participants but also effectively facilitates demand response in the smart grid, with respect to social welfare, satisfaction ratio, social efficiency, and computational overhead.

Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities

Abstract: With the advent of advancements in the power sector, various new methods have been devised to meet modern society’s electricity needs. To cope with these large sets of electronic device’s current requirements, better energy distribution is needed. Smart Grid (SG) facilitates energy providers to distribute electricity efficiently to the user according to their particular requirements. Recent advancements enable SG to monitor, analyze, control and coordinate for the demand and supply of electricity efficiency and energy saving. SG also allows two-way real-time communication between utilities and customers using cloud and Fog enabled infrastructures. SG minimizes management and operations cost, electricity theft, electricity losses, and maximize user comfort by giving the user choice about their energy use. It also facilitates Renewable Energy Resources (RER) and electric vehicles. Blockchain is a promising technology, provides the necessary features to solve most of these issues. Current Issues include saving a large amount of data, deletion, tampering, and revision of data. It also eliminates the necessity of intermediaries. Inherent security, along with the distributed nature, makes it a perfect candidate for improving the overall services. The rules of the smart contract are automatically enforced upon execution. Smart contracts are enhanced in a way that per-unit price is calculated dynamically based upon RER and utilities generated energy units in the overall grid. The system is also automated in a way that electricity is transferred from one resident (or service) to another resident according to their requirements. The exchange of energy is done via a smart contract after checking the needs of each participant. Each participant defines their requirements at the time of the registration and can update these thresholds. The privacy protection scheme has higher security, shown by theoretical security analysis. The main contributions of our work are two-fold; Using smart contracts to automate the bidding process for transactions based upon supply and demand for energy in smart cities. Secondly, at the same time, using hyper ledger fabric and composer to leveraging Blockchain to uphold privacy, anonymity, and confidentiality at the same time giving the users ability to have dynamic pricing based on supply and demand.

A Pool Strategy of Microgrid in Power Distribution Electricity Market

Abstract: This paper discusses a market-based pool strategy for a microgrid (MG) to optimally trade electric power in the distribution electricity market (DEM). The increasing penetration levels of distributed energy resources and MGs in distribution system (DS) stress distribution system operator and require higher levels of coordinated control strategies. The distribution system operator has limited visibility and control over such distributed resources. To reduce the complexity of the system and improve the efficiency of the electricity market operation, we propose a decentralized pool strategy for an MG to integrate with a distribution system through a market mechanism. A market-based interactions procedure between MGs and DS is developed for MGs as price makers to find an optimal bidding/offering strategy efficiently. To achieve a market equilibrium among all entities, we initially cast this problem as a bi-level programming problem, in which the upper level is an MG optimal scheduling problem and the lower level presents a DEM clearing mechanism. The proposed bi-level model is converted to a single mix-integer model, which is easier to solve. Uncertainties associated with MG's rivals’ offers and demands’ bids are considered in this problem. The solution results from a modified IEEE 33-Bus distribution system are presented and discussed. Finally, some conclusions are drawn and examined.

Two-Stage Bidding Strategy for Peer-to-Peer Energy Trading of Nanogrid

Abstract: With more distributed energy resources penetrated into the residential community, nanogrid based peer-to-peer (P2P) energy market has rapidly emerged over recent years. Due to the complexities on the decision-making process of each market participant, an efficient, fair and beneficial oriented bidding strategy is thus necessary. In this article, a two-stage bidding strategy for P2P trading of nanogrid is proposed. To overcome the limitations of traditional methods, in the first stage, a supply-demand relationship considered two-step price predictor, which aims to promote the usage of local renewable energy, is formulated to provide the guidance on transaction adjustment. In the second stage, trading preference based simultaneous game-theoretic approach is fully introduced, which can optimize the market equilibrium and then increase the social welfare of the P2P market. Additionally, to mitigate the possible failure of price matching, value-at-risk is implemented through the trading process as a risk hedging tool. To verify the effectiveness of the proposed strategy, usages of local renewable energy, economic benefits and success rates of transaction is compared against the traditional method for various cases.

An Energy Sharing Game With Generalized Demand Bidding: Model and Properties

Abstract: This paper proposes a novel energy sharing mechanism for prosumers who can produce and consume. Different from most existing works, the role of individual prosumer as a seller or buyer in our model is endogenously determined. Several desirable properties of the proposed mechanism are proved based on a generalized game-theoretic model. We show that the Nash equilibrium exists and is the unique solution of an equivalent convex optimization problem. The sharing price at the Nash equilibrium equals to the average marginal disutility of all prosumers. We also prove that every prosumer has the incentive to participate in the sharing market, and prosumers’ total cost decreases with increasing absolute value of price sensitivity. Furthermore, the Nash equilibrium approaches the social optimal as the number of prosumers grows, and competition can improve social welfare.