Real-time Market Prices Research Articles

Energy-agile design for parallel HPC applications

While there has been significant prior research on improving the energy-efficiency of parallel applications, there has been much less on optimizing them for green energy sources, which expose rapid changes in power's availability (or cost) due to the use of local renewable energy (or utility demand response programs). In this article, we present energy management policies that utilize active and inactive power capping to improve the performance of rigid and elastic parallel tasks when subject to variable power constraints from green energy sources. We deploy our policies on a real cloud testbed, and evaluate the performance with three different parallel applications. Our results demonstrate the effectiveness of green energy management policies with variable power. For example, we show that a reference supercomputer benchmark application requires 17% more time and 9% more energy to complete when power varies based on real-time electricity prices versus when power is unlimited at a fixed price; however, since the real-time spot market prices are lower than fixed prices, the total electricity cost of our best energy management policy when using real-time prices is 67% less than when using fixed prices.

Carbon trading’s impact on California’s real-time electricity market prices

What is the extent of a real-time electricity market’s pass-through of the marginal cost of CO2 emissions due to a cap-and-trade (C&T) program? This is an important policy question, as an incomplete pass-through would suggest the program’s limited effectiveness in achieving efficient pricing of electricity. To answer the question, we perform a regression analysis of California’s electricity market data for a 65-month period of 01/01/2011–05/31/2016. Based on this newly constructed large sample, we find that the California Independent System Operator’s real-time market prices contain a CO2 premium that closely tracks the marginal cost of CO2 emissions of California’s natural-gas-fired generation units, which are often at margin that determines the power prices. While the CO2 premium provides much needed incentives for renewable energy development, it does little to improve the incentive for natural-gas-fired generation investment in California. Hence, procurement of dispatchable generation capacity via long-term contracts continues to be useful for the state to meet the mandatory criteria for resource adequacy and system reliability.

A multi-follower bilevel stochastic programming approach for energy management of combined heat and power micro-grids

This paper presents a multi-follower bilevel programming approach to solve the 24-h decision-making problem faced by a combined heat and power (CHP) based micro-grid (MG). The framework contains the interests of two different agents: the MG operator/owner (MGO), who procures the maximization of total profit incurred in attending the forecasted demand of consumers via demand response program (DRP) as well as day-ahead (DA) and real-time (RT) markets participation, and the various CHP owners (CHPOs) who procure the maximization of the profits obtained from the thermal and electrical energy sales. The interaction between the entities is determined in a bilateral contract. Further, to deal with various uncertainties, each level is formulated as a stochastic two-stage problem, where the volatility nature of consumers' loads, RT market price and wind speed uncertainties are modeled using autoregressive moving average (ARMA) technique. In this paper, in order to consider realistic model of the problem, on the contrary to the most CHP-based MG scheduling literature, the network operation constraints such as voltage magnitude of buses and line flow limits are taken into account.

Optimal bidding strategy for an energy hub in energy market

An energy hub, as an active element in smart distribution grid, can participate in the day-ahead market via submitting bids to maximize its profit. The multi-input and multi-output energy vectors make energy hub different from other active elements. In this paper, a comprehensive optimal bidding strategy for an energy hub is modeled. The proposed model enables the energy hub to benefit from day-ahead and real-time markets. Stochastic optimization is proposed in this strategy to handle several market uncertainties consisting of day-ahead market prices, real-time market prices, and wind generation. The model takes advantages of multi-inputs vector of energy hub to submit the optimal bids including electricity selling/buying and optimizes the cost. Moreover, it handles the coupling between different types of loads. The problem is modeled as a mixed integer linear program. Numerical simulations evaluate the proposed model.

Energy Storage Arbitrage Under Day-Ahead and Real-Time Price Uncertainty

Electricity markets must match real-time supply and demand of electricity. With increasing penetration of renewable resources, it is important that this balancing is done effectively, considering the high uncertainty of wind and solar energy. Storing electrical energy can make the grid more reliable and efficient and energy storage is proposed as a complement to highly variable renewable energy sources. However, for investments in energy storage to increase, participating in the market must become economically viable for owners. This paper proposes a stochastic formulation of a storage owner's arbitrage profit maximization problem under uncertainty in day-ahead and real-time market prices. The proposed model helps storage owners in market bidding and operational decisions and in estimation of the economic viability of energy storage. Case study results on realistic market price data show that the novel stochastic bidding approach does significantly better than the deterministic benchmark.

Robust Optimal Operation of AC/DC Hybrid Microgrids Under Market Price Uncertainties

Energy management system (EMS) is responsible for the optimal operation of microgrids. EMS adjusts its operational schedule for near future by using the available information. Market price signals are generally used for the operation of microgrids, which are obtained by using estimation/ forecasting methods. However, it is difficult to precisely predict the market prices due to the involvement of various complex factors like weather, policy, demand, errors in forecasting methods, and fuel cost. Therefore, in this paper, the uncertainties associated with the real-time market price signals (buying and selling) are realized via a robust optimization method. In addition to market price signals, uncertainties associated with renewable power sources and forecasted load values are also considered. Initially, a deterministic model is formulated for an ac/dc hybrid microgrid. Then a min–max robust counterpart is formulated by considering the worst-case uncertainties. Finally, an equivalent mixed integer problem is formulated by using linear duality and other optimality conditions. The developed model can provide feasible solutions for all the scenarios if the uncertainties fluctuate within the specified bounds. The effect of market price uncertainties on internal power transfer and external power trading, operation cost, the state-of-charge of energy storage elements, and unit commitment of dispatchable generators is analyzed. Taguchi’s orthogonal array (OA) method is used to find the worst-case scenario within the specified uncertainty bounds. Then, Monte Carlo method is used to generate various scenarios within the uncertainty bounds to evaluate the robustness of the selected scenario via Taguchi’s OA method. Finally, a violation index is formulated to evaluate the robustness of the proposed approach against the deterministic model. Simulations results have validated the robustness of the proposed optimization strategy.

Electricity Cost Minimization for Delay-tolerant Basestation Powered by Heterogeneous Energy Source

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day’s electricity cost of individual BS.

Dynamic incentive framework for demand response in distribution system using moving time horizon control

This study presents two-level static and dynamic responsive load scheduling strategies for optimal tradeoff between scheduling preferences of demand response provider (DRP), demand response provider agent (DRPA) and distribution system operator (DSO) using moving time horizon control for real-time scheduling. The first level comprises of DRP scheduling with the objective of minimizing the electricity bill payments. The second stage includes a rescheduling strategy aided by DSO scheduling preferences constrained by the economic preferences of consumers set up in the first level of the scheduling strategy. There are two strategies for the second level: namely, static residential demand response (S-RDR), and dynamic residential demand response (D-RDR), which differ by the time sequence of operation and interactions between DRPA and DRPs. In S-RDR, the interactions between the two are limited to single event-acknowledge policy wherein all the DRPs act to schedule their responsive loads in a day ahead fashion. However, in D-RDR, multiple/continuous iterations take place between DRPA and DRPs with the consideration of optimal performance with respect to real-time market prices. The proposed framework is simulated using IEEE 34 bus distribution system. The simulation results are presented and discussed to analyse and compare the performance of proposed scheduling strategies with tariff-based and centralized scheduling approaches.

Hybrid IS-VWAP Dynamic Algorithmic Trading via LQR

For optimal trade execution, static models are useful for pre-trade estimation or benchmarking, but real trading must be based on dynamic decision making or dynamic programming (DP). DP algorithms can adjust execution automatically to real-time market price waves as well as inventory positions, and thus are more responsive and realistic. Development of DP models however faces a few notorious obstacles including the curse of dimensionality, absence of closed-form solutions for general formulations, and difficulty in incorporating hard constraints. The DP approach based on linear-quadratic regulators (LQR) was first introduced to algorithmic trading by Hora (2006). In Dynamic Control Theory, LQR models allow closed-form solutions thanks to the Bellman equations and quadratic value functions. The resulted optimal policies are affine functions of the state variables. The current work improves the LQR model of Hora (2006) in several important areas, including (a) the unconditional stability (or convexity), (b) retiring of the second-order small term of stagewise risk aversion, (c) quasi risk aversion achieved by time-varying delay costs, (d) incorporation of the bid-offer spreads, and (e) hybridizing the implementation shortfall (IS) algorithm with the VWAP approach to softly enforce the constraint of completion. The impact models and price dynamics still follow the earlier framework of Huberman and Stanzl (2001). The motivations behind every component of the improved model are elaborated, and the closed-form LQR solutions are derived (Theorem 1 and 2). Numerical simulation of the LQR trading paths is presented, which confirms several desirable properties of the hybrid IS-VWAP model.

California's renewable generation and pumped hydro storage's profitability

An analysis of 860 daily observations finds that rising renewable generation does not significantly diminish a pumped hydro storage system's daily operating profits from energy sales at the day-head and real-time market prices. The system, however, faces severely inadequate investment incentive because its annual operating profit can hardly pay for its annual fixed cost.

Systematic literature review on electronic reverse auction: issues and research discussion

Electronic reverse auctions (eRAs) are receiving extensive attention as an effective stratagem to negotiate prices with suppliers and service providers for the purchase of goods and services. The aim of this paper is to explore the literature to investigate the evolution of eRA over the past few years. A review of literature on 'eRA' from 2001 to 2015 has been undertaken. The subject has been discussed at length from the viewpoint of 'supplier participation', 'spend category' and 'auction size'. The present study consolidates various benefits of eRAs such as reduced time and prices for buyers, increased competition among sellers, discovery of real time market price and improved efficiency of the purchasing process. It also discusses the limiting factors such as costs of switching suppliers, challenges to buyer-supplier relationship and resistance on the part of buyers to be a part of the entire process.

Systematic literature review on electronic reverse auction: issues and research discussion

Electronic reverse auctions (eRAs) are receiving extensive attention as an effective stratagem to negotiate prices with suppliers and service providers for the purchase of goods and services. The aim of this paper is to explore the literature to investigate the evolution of eRA over the past few years. A review of literature on 'eRA' from 2001 to 2015 has been undertaken. The subject has been discussed at length from the viewpoint of 'supplier participation', 'spend category' and 'auction size'. The present study consolidates various benefits of eRAs such as reduced time and prices for buyers, increased competition among sellers, discovery of real time market price and improved efficiency of the purchasing process. It also discusses the limiting factors such as costs of switching suppliers, challenges to buyer-supplier relationship and resistance on the part of buyers to be a part of the entire process.

Relative pricing of binary options in live soccer betting markets

Live soccer betting markets differ from other binary options markets in that all fundamental information is observable, the options mature in less than two hours and the markets are highly liquid. This study presents a new method for the identification of hidden information in market prices. The method is based on two independent Poisson distributions and on a numerical algorithm for the aggregation of all market price information into one rational number. The method is applied to an empirical dataset of real time market prices in 29,413 soccer games. The results indicate that the method selects the most profitable markets and allows for a significant improvement in average investment returns.

Microgrid Scheduling With Uncertainty: The Quest for Resilience

In recent years, natural disasters around the world have underscored the need for operative solutions that can improve the power grid resilience in response to low-probability high-impact incidents. The advent of microgrids (MGs) in modern power systems has introduced promising measures that can fulfil the power network resiliency requirements. This paper presents a two-stage stochastic programing approach to the optimal scheduling of a resilient MG. The impact of natural disasters on the optimal operation of MGs is modeled using a stochastic programming process. Other prevailing uncertainties associated with wind energy, electric vehicles, and real-time market prices are also taken into account. The proposed hourly scheme attempts to mitigate damaging impacts of electricity interruptions by effectively exploiting the MG capabilities. Incorporating AC network constraints in the proposed model offers a better solution to the security-constrained operation of MGs. The proposed model is linearized which offers robustness, simplicity, and computational efficiency in optimizing the MG operation. The effectiveness of proposed approach is illustrated using a large-scale MG test bed with a realistic set of data.

Mlouma: to connect the agricultural products market players

Subject area Social entrepreneurship/social innovation. Study level/applicability This real case targets license and master’s students as part of an ongoing course on social entrepreneurship, social innovation, in one word, on entrepreneurship. Case overview Mlouma is a platform based on Web, SMS, USSD, mobile application and call center. This social enterprise keeps farmers in contact with food buyers by displaying real-time market prices and localizations. The service will improve the efficiency of the agriculture supply chain, helping farmers to get a better price for their product. By introducing information and communication technology, Mlouma provides innovative solutions to the market failure by creating more value for producers and consumers. Expected learning outcomes This case shows the importance of the entrepreneurial orientation of social enterprises to adapt to its context and discuss theory of social entrepreneurship. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 3: Entrepreneurship.

Merit-order effects of renewable energy and price divergence in California’s day-ahead and real-time electricity markets

We answer two policy questions: (1) what are the estimated merit-order effects of renewable energy in the California Independent System Operator’s (CAISO’s) day-ahead market (DAM) and real-time market (RTM)? and (2) what causes the hourly DAM and RTM prices to systematically diverge? The first question is timely and relevant because if the merit-order effect estimates are small, California’s renewable energy development is of limited help in cutting electricity consumers’ bills but also has a lesser adverse impact on the state’s investment incentive for natural-gas-fired generation. The second question is related to the efficient market hypothesis under which the hourly RTM and DAM prices tend to converge. Using a sample of about 21,000 hourly observations of CAISO market prices and their fundamental drivers during 12/12/2012–04/30/2015, we document statistically significant estimates (p-value≤0.01) for the DAM and RTM merit-order effects. This finding lends support to California’s adopted procurement process to provide sufficient investment incentives for natural-gas-fired generation. We document that the RTM-DAM price divergence partly depends on the CASIO’s day-ahead forecast errors for system loads and renewable energy. This finding suggests that improving the performance of the CAISO’s day-ahead forecasts can enhance trading efficiency in California’s DAM and RTM electricity markets.

Zonal merit-order effects of wind generation development on day-ahead and real-time electricity market prices in Texas

This paper uses a regression-based approach to explore the impact of wind generation development on wholesale electricity prices in the Electric Reliability Council of Texas (ERCOT) market. We find that wind generation development has a greater effect on real-time market (RTM) prices than day-ahead market (DAM) prices. Higher wind generation forecast errors tend to reduce the RTM prices, chiefly because unanticipated increases in wind generation reduce the real-time net loads to be served by fossil fuel power plants. Improving ERCOT’s load and wind generation forecast accuracy tends to make the DAM and RTM prices converge, thus enhancing ERCOT’s market trading efficiency. Finally, the estimated merit-order effects are greatest in the ERCOT zones where the wind generation capacity locally resides.

Optimal Bidding, Scheduling, and Deployment of Battery Systems in California Day-Ahead Energy Market

An optimal supply and demand bidding, scheduling, and deployment design framework is proposed for battery systems. It takes into account various design factors such as the day-ahead and real-time market prices and their statistical dependency, as well as the location, size, efficiency, lifetime, and charge and discharge rates of the batteries. Utilizing second-life/used batteries is also considered. Without loss of generality, our focus is on the California Independent System Operator (ISO) energy market and its two available bidding options, namely self-schedule bidding and economic bidding. While the formulated stochastic optimization problems are originally nonlinear and difficult to solve, we propose a methodology to decompose them into inner and outer subproblems. Accordingly, we find the global optimal solutions within a short amount of computational time. All case studies in this paper are based on real market data.

Bidding Strategy for Microgrid in Day-Ahead Market Based on Hybrid Stochastic/Robust Optimization

This paper proposes an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG, and price responsive loads. The microgrid coordinates the energy consumption or production of its components, and trades electricity in both day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, and day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total cost of operation minus total benefit of demand. This formulation can be solved by mixed-integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a photovoltaic panel, a fuel cell, a micro-turbine, a diesel generator, a battery, and a responsive load show the advantage of stochastic optimization, as well as robust optimization.

Capacity Controlled Demand Side Management: A Stochastic Pricing Analysis

We consider a novel paradigm for demand side management, assuming that an aggregator communicates with a household only at the meter, imposing a capacity constraint, i.e., a restriction on the total power consumption level within a given time frame. Consumers are then responsible to adjust the set-points of the individual household devices accordingly to meet the imposed constraint. We formulate the problem as a stochastic household energy management program, with stochasticity arising due to local photovoltaic generation. We show how a demand bidding curve for capacity increments can be constructed as a by-product of the developed problem and provide a rigorous pricing analysis that results in a probabilistic “shadow” price envelope. To evaluate the efficacy of the proposed approach, we compare it with an idealized real-time market price set-up and show how our analysis can provide guidelines to consumers when selecting a service contract for load curtailment.