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The utilization of wind energy sources with energy storage systems has been increased in the power sector to satisfy the consumer’s energy demand with minimum price. This paper presents the impact of a wind farm (WF) and pumped hydroelectric storage (PHS) system in the competitive electricity market under a congested transmission system. The PHS system is used to compensate for the deviation of WF generation in the real-time electricity market. To investigate the impact of the proposed method, initially, the market-clearing power problem is solved without consideration of WF and PHS systems, and again it is solved with the WF and PHS systems. The optimal location of the WF and PHS systems is decided by the bus sensitivity factor (BSF) of these systems. The analysis is carried out by using generator sensitivity factor (GSF) with the help of the moth flame optimization (MFO) algorithm and thereby calculating market clearing price (MCP) and market clearing volume (MCV). The MFO algorithm is used here for the first time for solving the congested market-clearing power problem with the integration of WF and PHS systems under deregulated environment. The presented approach shows the improvement of social welfare after the placement of WF and PHS in the congested deregulated system. Modified IEEE 30 bus system is used to solve the market-clearing power problem and results obtained from the MFO algorithm are compared with the firefly algorithm (FA). Three different real-time wind speed data have been considered here to verify the proposed approach with uncertainty and the continuously changing nature of wind flow. It is discovered that social welfare is improved with the quantity addition of wind power, regardless of optimization techniques.

I study tâtonnement processes in a matching market without transfers. In each period, schools set cutoffs, i.e., the preference ranks of the least preferred students they are willing to admit, and students accept their most preferred offers. Cutoffs are adjusted on the basis of demand–supply imbalances. A school's adjustment from one period to the next is moderate if it is bounded by the most recently observed imbalance at that school. I show that for any period in which all schools adjust moderately, the sum of demand–supply imbalances across all schools weakly decreases. Moreover, if all schools always adjust moderately and there is a unique stable matching, then adjustments converge to a market‐clearing cutoff vector. If there is more than one stable matching, moderate adjustments may cycle indefinitely, but the supremum and the infimum of all cutoff vectors observed along a cycle are both market‐clearing.

This study proposes a multi‐agent‐based framework for Peer‐to‐Peer (P2P) power trading in a locality electricity market (LEM) for self‐interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win–win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day‐ahead market. LETS computes the internal market prices employing any one of the market‐clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation‐demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27–68% under different operating conditions.

Leading to the enhancement of smart grid implementation, the peer-to-peer (P2P) energy transaction concept has grown dramatically in recent years allowing the end-users to successfully exchange their excess generation and demand in a more profitable way. This paper presents local energy market (LEM) architecture with various market strategies for P2P energy trading among a set of end-users (consumers and prosumers) in a smart residential locality. In a P2P fashion, prosumers/consumers can export/import the available generation/demand in the LEM at a profit relative to utility prices. A common portal known as the transactive energy market operator (TEMO) is introduced to manage the trading in the LEM. The goal of the TEMO is to develop a transaction agreement among P2P players by establishing a price for each transaction based on the price and trading demand provided by the participants. A few case studies on a location with ten residential P2P participants validate the performance of the proposed TEMO.

A new model of consumer behavior is developed using a hybrid of cognitive psychology and microeconomics. The development of the model starts with the mental coding of combinations of gains and losses using the prospect theory value function. Then the evaluation of purchases is modeled using the new concept of \"transaction utility.\" The household budgeting process is also incorporated to complete the characterization of mental accounting. Several implications to marketing, particularly in the area of pricing, are developed. This article was originally published in Marketing Science , Volume 4, Issue 3, pages 199–214, in 1985.

In this paper we present a multicommodity bandwidth exchange model for balancing aggregated communication bandwidth resources (BACBR) that allows us to aggregate similar offers. In this model offers submitted to sell (or buy) the same, similar, or equivalent network resources (or demands for end-to-end connections) are aggregated into single commodities. BACBR model is based on the balancing communication bandwidth trade (BCBT) model. It requires much less variables and constraints then original BCBT, however the outcomes need to be disaggregated. The general model for disaggregation is also given in the paper.

We explore marketplace design in the context of a business-to-business platform specializing in liquidation auctions. Even when the platform’s aggregate levels of supply and demand remain fixed, we establish that the platform’s ability to use its design levers to manage the availability of supply over time yields significant value. We study two such levers, each using the platform’s availability of supply as a means to incentivize participation from buyers who decide strategically when/how often to participate. First, the platform’s listing policy sets the ending times of incoming auctions (hence, the frequency of market clearing). Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% mainly by inducing a higher level of bidder participation. The second lever is a recommendation system that can be used to reveal information about real-time market thickness to potential bidders. The optimization of these levers highlights a novel trade-off. Namely, when the platform consolidates auctions’ ending times, more bidders may participate in the marketplace (demand-side competition); but ultimately auctions for substitutable goods cannibalize one another (supply-side competition). To optimize these design decisions, we estimate a structural model that endogenizes bidders’ dynamic behavior, that is, their decisions on whether/how often to participate in the marketplace and how much to bid. We find that appropriately designing a recommendation system yields an additional revenue increase (on top of the benefits obtained by optimizing the platform’s listing policy) by reducing supply-side cannibalization and altering the composition of participating bidders. This paper was accepted by Vishal Gaur, operations management .

We consider a pure exchange economy, where for each good several trading institutions are available, only one of which is market-clearing. The other feasible trading institutions lead to rationing. To learn on which trading institutions to coordinate, traders follow behavioral rules of thumb that are based on the past performances of the trading institutions. Given the choice of institutions, market outcomes are determined by an equilibrium concept that allows for rationing. We find that full coordination on the market-clearing institutions without any rationing is a stochastically stable outcome, independently of the characteristics of the alternative available institutions. We also find, though, that coordination on certain other, non-market-clearing institutions with rationing can be stochastically stable.

In the paper the market model for balancing communication bandwidth trade (BCBT) is analyzed in the form of multi-commodity market data model (M3). The distinguishing feature of BCBT model is that it assumes that market players can place buy offers not only for isolated network resources – inter-node links, but also for end-to-end network paths of predefined capacity, that is, every offer concerns a point-to-point bandwidth connection between a pair of specified locations in a communication network. The model enables effective balancing of sell and buy offers for network resources in a way which maximizes global economic welfare. The open multi-commodity market data model provides efficient and clear mechanisms, which support the environment of auctions and multi-commodity exchanges, especially when the trade is constrained by the infrastructure resources. Thus the model may be used in designing open information systems for market balancing and clearing in the context of multicommodity trade in various network infrastructure sectors.

Belief disagreements have been suggested as a major contributing factor to the recent subprime mortgage crisis. This paper theoretically evaluates this hypothesis. I assume that optimists have limited wealth and take on leverage so as to take positions in line with their beliefs. To have a significant effect on asset prices, they need to borrow from traders with pessimistic beliefs using loans collateralized by the asset itself. Since pessimists do not value the collateral as much as optimists do, they are reluctant to lend, which provides an endogenous constraint on optimists' ability to borrow and to influence asset prices. I demonstrate that the tightness of this constraint depends on the nature of belief disagreements. Optimism concerning the probability of downside states has no or little effect on asset prices because these types of optimism are disciplined by this constraint. Instead, optimism concerning the relative probability of upside states could have significant effects on asset prices. This asymmetric disciplining effect is robust to allowing for short selling because pessimists that borrow the asset face a similar endogenous constraint. These results emphasize that what investors disagree about matters for asset prices, to a greater extent than the level of disagreements. When richer contracts are available, relatively complex contracts that resemble some of the recent financial innovations in the mortgage market endogenously emerge to facilitate betting.