Multi-market Environment Research Articles

Risk constraint techno-economic assessment of photovoltaic-penetrated distribution network considering hydrogen vehicles in a multi-market environment

Challenges arising from increasing the penetration roof-top photovoltaic (PV) system are mostly related to the uncertainties concerned with PV generation units, which significantly impact the optimal operation of the power grid and may impose economic risks on the network operator. This article analyzes the real-time and day-ahead financial risk of a PV penetrated smart distribution grid with hydrogen-powered vehicles demands. The proposed approach models the uncertainties using a scenario-based method and evaluates the resultant risk of these uncertainties by utilizing the downside risk constraint (DSRC) approach. Compared to other risk-measures, DSRC method is a convex non-equilibrium approach and provides a closed-loop solution which is incorporated explicitly as a constraint in the problem. Besides, due to the linearity of the proposed DSRC method, there is no increased computational burden of the final risk-based model. The uncertainties involved in the model include PV generation, electrical demand, real-time market price, and behavior of hydrogen-powered vehicles. The studied test system is the standard IEEE 33 bus distribution network which 14 buses have installed PV with capacity of 50-150% of their peak load. The results validate a 100% reduction of expected downside risk (EDR), while the total cost is increased only by 3.8%.

Power Distribution Expansion Planning in the Presence of Wholesale Multimarkets

By deregulating the countries' electricity industry, the market players must adapt their performance to the deregulated environment. In this article, a risk-based stochastic optimization framework is proposed to model the participation of distribution system operator (DSO) in distribution expansion planning (DEP) problems in the presence of electricity wholesale multimarkets. Based on the proposed methodology in this article, the DSOs will be able to invest in the DEP by taking the benefits and imposed risks of the existing electricity multimarkets, i.e., forward contract, day-ahead, and balancing markets, to manage the risks of long-term uncertainties. The DSOs, who are the only retail suppliers in a specific region, can cover their DEP costs by offering fair retail prices to the retail consumers. Besides, DSOs could form diverse long- and short-term portfolios from the different electricity markets to procure the forecasted real loads and loss power of the network over the planning horizon. The diverse portfolio of the suppliers and a fair retail price can satisfy the DSOs to consider the multimarkets in the DEP process. Furthermore, the risk of uncertainties is modeled using the method of conditional value at risk (CVaR). Based on the obtained results, the DSO can reduce network loss costs by procuring power from markets rather than increasing the installed branches' ampacity by considering multimarkets and market price uncertainties. Besides, the obtained profit of DSO is increased by 12.16% in the proposed electricity multimarket environment.

An efficient multi-agent negotiation algorithm for multi-period photovoltaic array reconfiguration with a hydrogen energy storage system

Since the partial shading conditions easily bring a significant energy loss for a photovoltaic system, various array reconfiguration techniques have been proposed to improve the power generation efficiency. The existing studies of photovoltaic array reconfiguration mainly attempted to maximize the power output, which easily leads to a low total profit since they did not take the multi-period power fluctuation into account. In general, a large power fluctuation will result in a high regulation cost in a frequency regulation market, which can be smoothed by a hydrogen energy storage system. Consequently, this paper constructs a new multi-period photovoltaic array reconfiguration with a hydrogen energy storage system under partial shading conditions. It aims to maximize the total profit of photovoltaic system instead of only the power output by simultaneously consider the electricity selling profit in an electricity market, the hydrogen selling profit in a hydrogen market, and the regulation cost in a frequency regulation market. To address this problem, a novel efficient multi-agent negotiation algorithm with an auctioneer and multiple bidders is designed with the multi-round negotiation and the random re-initialization. The comprehensive case studies with a 10 × 10 total-cross-tied photovoltaic array shows that the proposed algorithm can acquire the higher total profit compared to five centralized meta-heuristic algorithms, in which the total profit can be increased from negative to positive against that without optimization under a discrete varying partial shading condition. Based on the simulation results, it can be concluded that the multi-period photovoltaic array reconfiguration can dramatically increase the total profit under a multi-market environment, while the proposed algorithm can achieve an efficient and distributed optimization under different partial shading conditions.

A long-term intelligent operation and management model of cascade hydropower stations based on chance constrained programming under multi-market coupling

Under the medium- and long-term electric markets, cascaded hydropower stations face a series of practical challenges due to the uncertainty of inflow and market price. For long-term dispatch scheduling, the allocation of power generation in multimarkets is critical, including clean energy priority consumption market, inter provincial market and intra provincial market in order to maximize the operator’s expected revenue and reduce the market operation risks. Based on the hydro-dominant electricity market structure and settlement rules, we propose a long-term optimal operation method for cascade hydropower stations considering the uncertainty of multiple variables. First, a seasonal autoregressive integrated moving average model is used to handle the time-varying and seasonal characteristics of inflow series by using a copula connect function to fit the joint distribution of the monthly inflow, the clearing price of the intra provincial market and the delivery volume of the inter provincial market. Then, uncertain chance-constrained programming is established. Finally, a developed particle swarm optimization algorithm embedded in a Monte Carlo simulation is solved for hydropower operation policies, and the maximum revenue, resource allocation and scheduling strategy are obtained under the corresponding risk tolerance. Taking the actual data of cascaded hydropower stations in Yunnan Province, China, as an example, a simulation analysis is carried out. The results show that the proposed method can reasonably describe the uncertainty and correlation between the variables, realizing the optimal allocation of resources among multimarkets, and provide references for the long-term optimal operation of cascade hydropower stations in a multimarket environment. The results also show that the decision strategies should be determined considering the decision-maker’s risk preference.

Long-Term Generation Scheduling for Cascade Hydropower Plants Considering Price Correlation between Multiple Markets

The large-scale cascade hydropower plants in southwestern China now challenge a multi-market environment in the new round of electricity market reform. They not only have to supply the load for the local provincial market, but also need to deliver electricity to the central and eastern load centers in external markets, which makes the generation scheduling much more complicated, with a correlated uncertain market environment. Considering the uncertainty of prices and correlation between multiple markets, this paper has proposed a novel optimization model of long-term generation scheduling for cascade hydropower plants in multiple markets to seek for the maximization of overall benefits. The Copula function is introduced to describe the correlation of stochastic prices between multiple markets. The price scenarios that obey the Copula fitting function are then generated and further reduced by using a scenario reduction strategy that combines hierarchical clustering and inconsistent values. The proposed model is applied to perform the long-term generation scheduling for the Wu River cascade hydropower plants and achieves an increase of 106.93 million yuan of annual income compared with the conventional scheduling model, without considering price scenarios, showing better performance in effectiveness and robustness in multiple markets.

Assessing cross-border integration of capacity mechanisms in coupled electricity markets

There is momentum in a number of European electricity markets towards the implementation of national generation capacity mechanisms. This renewed interest in capacity mechanisms raises the question of the cohabitation of both relatively well-integrated short-term energy markets and national generation capacity mechanisms. This paper examines a key issue of generation adequacy policies in a multi-market environment: the effect of foreign generators’ and interconnectors’ inclusion in national capacity mechanisms. The results show that the absence of cross-border participation could lead to significant social welfare losses associated with over and under-capacity procuring risk. This implies a vicious circle: as capacity mechanisms over or under-procure capacity, cross-border trade of electricity becomes more distorted, which in turn undermines the effectiveness of capacity mechanisms themselves. The findings also show that the inclusion of interconnectors in national capacity mechanisms could induce investments in merchant interconnections by compensating for network externalities and adjusting profit levels on the basis of the interconnection costs. However, despite the participation of interconnectors in capacity mechanisms, the exclusion of foreign generation of this market-based scheme undermines efficiency. In the absence of a wider EU single capacity mechanism, the inclusion of foreign generators and interconnectors in national capacity mechanisms should ensure the most efficient cohabitation of the EU Single Market and national capacity mechanisms.

Optimal Portfolio Selection of GenCo Under Congestion Risk in Multimarket Environment

In a restructured electricity market derivative instruments help in reducing price risk. This paper proposes a portfolio optimization model considering uncertainty of electricity spot prices. Using this model, a thermal generation company (GenCo) holding contracts within and outside its jurisdiction can maximize its profit with limited risk exposure. The proposed model is formulated using mean variance portfolio theory, considering spot market, bilateral contracts and options with the possibility of managing congestion risk while trading between different locations. To handle uncertainties scenario generation and reduction techniques are used. The spot price inaccuracies are further represented as scenario tree. The producers risk preference is expressed by a utility function as the trade-off between expectation and variance of the return. For multiple scenarios the optimization problem is solved to obtain a stochastic solution to the optimal capacity allocation problem. The work addresses price variability of zonal transaction considering both physical and financial contracts to schedule the output of an opportunistic GenCo for maximizing its profit by creating hedging strategy through multiple scenarios. The results indicate that the proposed model is capable of improving the profit risk trade-off of the portfolios. GenCos profit seeking behavior during congestion relieving situation has also been demonstrated in the proposed risk modeling. Study is performed on a GenCo situated in NORDPOOL market.

Taxing financial transactions in fundamentally heterogeneous markets

The recent global financial crisis has revived a long-standing debate on the desirability and feasibility of taxing financial activities to curb speculation and promote price stability. In this paper we apply agent-based computational techniques to explore this issue in a multi-market environment in which the processes driving the fundamental value of the securities traded in different jurisdictions are heterogeneous. A natural exemplification is to assume that security dealers have the opportunity to submit orders by choosing among stock markets at different stages of development. We argue that the proper policy objective to be pursued is not volatility in itself but price efficiency, that is, the volatility in excess of the discounted stream of subsequent dividends. In this case, a global coordination of tax rates is incentive-compatible, given that it minimizes the distortion associated with speculative trading, on the one hand, and it ensures that the loss of trading volume is lower if compared to the case of unilateral taxation on the other. Notwithstanding a fundamental heterogeneity of the markets involved, the optimal tax rate turns out to be symmetric provided that fundamental value trajectories are positively correlated.

Option Trading Leverage and Stock Returns

Informed investors choose optimal leverage as a function of risk and cost. In the option market, they establish the chosen exposure using options of low leverage which are more liquid. On the other hand, noise traders prefer options of high leverage which have more lottery-like payoff. Such divergent preferences provide an opportunity to detect informed trading in the option market. In this paper I examine volume-weighted average of option leverage named Option Trading Leverage Ratio (OTLR). Portfolios with the lowest OTLR underperform portfolios with the highest OTLR by 1.28% monthly or 15% annually. Predicative relationship between OTLR and earnings-related information indicates that better skills in processing public information contributes to the advantage of informed option investors. Empirical findings are consistent with low OTLR capturing negative informed option trading due to the short-sale constraints of the stock market and liquidity concern of sophisticated investors. This paper contributes to the understanding of information flow in a multi-market environment.

Coordination of Circuit Breakers? Volume Migration and Volatility Spillover in Fragmented Markets

We study circuit breakers in a fragmented, multi-market environment and investigate whether a coordination of circuit breakers is necessary to ensure their effectiveness. In doing so, we analyze 2,337 volatility interruptions on Deutsche Boerse and research whether a volume migration and an accompanying volatility spillover to alternative venues that continue trading can be observed. Different to prevailing theoretical rationale, trading volume on alternative venues significantly decreases during circuit breakers on the main market and we do not find any evidence for volatility spillover. Moreover, we show that the market share of the main market increases sharply during a circuit breaker. Surprisingly, this is amplified with increasing levels of fragmentation. We identify high-frequency trading as a major reason for the vanishing trading activity on the alternative venues and give empirical evidence that a coordination of circuit breakers is not essential for their effectiveness as long as market participants shift to the dominant venue during market stress.

Multimarket Exit, Strategic Merger, and Collusion

We study the duopoly exit equilibrium in a multimarket environment with declining demand, and investigate the roles of strategic merger, agency cost, and collusion in a multimarket exit game. Cross-market spillovers with endogenous duration and the strategic merger of outside firms can serve as sources of commitment in a multipoint war of attrition, and might help large firms successfully force their smaller rivals out. Agency cost can be beneficial in bolstering strategic commitment in declining markets. Under certain conditions, multimarket contact might facilitate collusion in a multimarket exit game where firms yield a market to each other reciprocally.

Taxing Financial Transactions in Fundamentally Heterogeneous Markets

The recent global financial crisis has revived a well-honored debate on the desirability and feasibility of taxing financial activities to curb speculation and promote price stability. In this paper we apply agent-based computational techniques to explore this issue in a multi-market environment in which the processes driving the fundamental value of the securities traded in different jurisdictions are heterogeneous. A natural exemplification is to assume that security dealers have the opportunity of submitting orders by choosing among stock markets at different stages of development. We argue that the proper policy objective to be targeted is not volatility in itself, but that in excess of the discounted stream of subsequent dividends, that is price efficiency. In this case, a global coordination is incentive-compatible, given that it minimizes the distortion associated to speculative trading on the one hand, and it ensures that the loss of trading volume is lower if compared to the case of unilateral taxation on the other one. Notwithstanding a fundamental heterogeneity of the markets involved, the optimal tax rate turns out to be uniform.

Internet Traffic Distribution Analysis in case of Multi-Operator and Multi-Market Environment of Computer Network

is widely used across the world to serve many purposes by millions of people every minute. This has given rise to many fold increase in internet based service providers. The internet traffic shared between these service providers gets jammed due to heavy load on the network. Naldi (2002) discussed model based internet traffic share problem in a different way, based on the assumption of call-by-call basis. Further, this idea of call-by-call improved and transformed to two-call-basis by Shukla and Gadewar (2010) and enhanced results arrived. In this paper market based internet traffic share has been calculated in terms of quality of services, initial preferences and other network parameters. Traffic share expressions for different kind of operators have been derived and simulation study is performed. It is found that traffic share is highly correlated with the location of market and network blocking probability.

Optimal trading of algorithmic orders in a liquidity fragmented market place

An optimization model for the execution of algorithmic orders at multiple trading venues is herein proposed and analyzed. The optimal trajectory consists of both market and limit orders, and takes advantage of any price or liquidity improvement in a particular market. The complexity of a multi-market environment poses a bi-level nonlinear optimization problem. The lower-level problem admits a unique solution thus enabling the second order conditions to be satisfied under a set of reasonable assumptions. The model is computationally affordable and solvable using standard software packages. The simulation results presented in the paper show the model’s effectiveness using real trade data. From the outset, great effort was made to ensure that this was a challenging practical problem which also had a direct real world application. To be able to estimate in realtime the probability of fill for tens of thousands of orders at multiple price levels in a liquidity fragmented market place and finally carry out an optimization procedure to find the most optimal order placement solution is a significant computational breakthrough.

Managing risk in multi-asset class, multimarket central counterparties: The CORE approach

Multi-asset class, multimarket central counterparties (CCPs) are becoming less uncommon as a result of merges between specialized (single-asset class, single market) CCPs and market demands for greater capital efficiency. Yet, traditional CCP risk management models often lack the necessary sophistication to estimate potential losses relative to the closeout process of a defaulter’s portfolio in a multi-asset class, multimarket environment. As a result, multi-asset class, multimarket CCPs usually rely on a simplified silo approach for risk calculation which not only fails to deliver efficiency, but may also increase systemic risk. The CORE (Closeout Risk Evaluation) approach, on the other hand, provides conceptual and mathematical tools necessary for robust and efficient central counterparty risk evaluation in multi-asset class and multimarket environments, acknowledging the portfolio dynamics involved in the closeout process as well as important “real life” market frictions.

A Multimarket Decision-Making Framework for GENCO Considering Emission Trading Scheme

In a multimarket environment, a GENCO produces electricity subject to a number of factors, including physical and environmental constraints, together with trading strategies in the electricity market (EM), fuel market (FM) and carbon market (CM). To assist a GENCO to maximize its profits from EM, FM and CM, this paper proposes a dynamic decision making model with two consecutive stages. Fuzzy differential evolution algorithm is used to solve this decision-making problem. Taking transactions in the three interactive markets into account, the proposed model has been tested for a GENCO consisting of seven thermal units and a wind farm. A rational tradeoff between the profit-making and emission reduction has been demonstrated by the GENCO using the proposed model, indicating a well alignment with the intended goal of the introducing emission trading scheme (ETS).

Impacts of emission trading schemes on GENCOs’ decision making under multimarket environment

The implementation of Emission Trading Schemes (ETS) in the electricity supply industry has driven generation companies (GENCOs) to put efforts to reduce their produced emissions. Under a multimarket environment, a GENCO generates electricity subject to physical, fuel and environmental constraints. Separate and evolving research efforts are currently shaping electricity market, fuel market and carbon market without paying adequate attentions to how each market affects the others, though the markets have overlapping goals with respect to the global economic and environmental benefits. Under this background, this paper investigates the impacts of carbon policies on a GENCO's decision making under multimarket environment. A dynamic decision making model is proposed to deal with the multimarket trading problem for a GENCO during each trading period. Differential Evolution (DE) algorithm is employed to solve the multi-period optimization problem for each time interval. Comparisons between different scenarios demonstrate the economic and environmental influences of different policies on a GENCO. With the proposed model, a GENCO can make a rational tradeoff between profit making and emission reduction under the three interactive markets environment. Policies defining the three interactive markets can accurately reflect the intended goals such as reducing emissions, promoting renewable and keeping electricity cost at a reasonable level.

Price discovery and trade fragmentation in a multi-market environment: Evidence from the MTS system

This paper proposes new metrics for the process of price discovery on the main electronic trading platform for euro-denominated government securities. Analysing price data on daily transactions for 107 bonds over a period of 27months, we find a greater degree of price leadership of the dominant market when our measures (as opposed to the traditional price discovery metrics) are used. We also present unambiguous evidence that a market’s contribution to price discovery is crucially affected by the level of trading activity. The implications of these empirical findings are discussed in the light of the debate about the possible restructuring of the regulatory framework for the Treasury bond market in Europe.

Price Discovery and Trade Fragmentation in a Multi-Market Environment: Evidence from the MTS System

This paper proposes new metrics for the process of price discovery on the main electronic trading platform for euro - denominated government securities. Analyzing price data on daily transactions for 107 bonds over a period of twenty - seven months, we find a greater degree of price leadership of the dominant market when our measures (as opposed to the traditional price discovery metrics) are used. We also present unambiguous evidence that a market’s contribution to price discovery is crucially affected by the level of trading activity. The implications of these empirical findings are discussed in the light of the debate about the possible restructuring of the regulatory framework for the Treasury bond market in Europe.

Optimal trading strategy for GenCo in LMP-based and bilateral markets using self-organising hierarchical PSO

This paper proposes an optimal trading strategy for a generation company (GenCo) in multi-market environment including day-ahead spot and long term bilateral contract markets using self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO-TVAC). The proposed trading strategy is formulated as a two-stage optimisation problem. Firstly, the GenCo’s objective model which is to maximise expected profit and to minimise risk of profit variation is solved by SPSO-TVAC. Secondly, the market clearing model which is to minimise system cost of locational marginal price (LMP) based market is solved by DC optimal power flow (DCOPF). The Monte Carlo method is employed to simulate other bidders’ behaviour in competitive environment. Test results on the PJM 5-bus system indicate that SPSO-TVAC is superior to inertia weight approach particle swarm optimisation (IWAPSO) and genetic algorithm (GA) in searching the optimal trading solution. In addition, different bilateral contract prices and spot demand significantly impact GenCos’ trading behaviour. Accordingly, the proposed approach could be a beneficial decision-making tool for a GenCo in energy trading. Keywords: Optimal bidding strategy, energy trading, locational marginal price, bilateral contract market, particle swarm optimisation, Monte Carlo simulation.