Day-ahead Wholesale Market Research Articles

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.

Economic Analysis of Electricity Storage Based on Heat Pumps and Thermal Storage Units in Large-Scale Thermal Power Plants

In this paper the financial viability of a novel storage concept, referred to as ‘integrated pumped-heat-electricity storage’, is assessed for both a coal-fired and a combined cycle (CC) power plant located in Germany, as well as for a concentrated solar power (CSP) plant located in Spain. The rationale of this concept is to use electricity during times of cheap wholesale market prices, e.g. stemming from a high supply of renewable energies, in order to generate thermal energy via a heat pump, since thermal energy can be stored at comparatively low cost. If the electricity demand rises again, the stored thermal energy is used to power a conventional water-steam cycle, thereby reducing the amount of fossil fuels used or enlarging the operating time of a CSP plant. A mixed integer linear program, considering the day-ahead wholesale market electricity prices and remunerations of offering tertiary control power in Germany, is employed to find an approximation of the optimal schedule for the generation in the power plant as well as for the purchase and the sale of electricity. The financial viability is assessed using net present value (NPV) and real options analysis. Storing electricity to profit from temporal arbitrage is found to be unprofitable, since the (conservatively estimated) costs of thermal storage units are still too high. The largest part of the revenues stems from the remunerations of offering tertiary control power. Therefore, while the utilization of heat pumps, which are estimated to have rather high costs, is not profitable, employing electric heaters without storage units is found to be economically viable for a coal-fired and a CC power plant in Germany. Here, NPV and real options analysis yield the same result, i.e. to invest immediately in such an application. In Spain, offering tertiary control power is presently not remunerated, but only the call-offs of tertiary control energy, which are not considered in the mixed integer linear program. Consequently, employing electric heaters in CSP plants in Spain is found to be not financially viable either.

Integration of nodal hourly pricing in day-ahead SDC (smart distribution company) optimization framework to effectively activate demand response

This paper focuses on using a new nodal hourly electricity pricing to maximize the profit of a LDC (local distribution company). The proposed pricing mechanism determines DA (day-ahead) hourly retail prices based on load specifications. These specifications include location, price elasticity and demand profile. Nodal hourly prices are determined in an optimization framework which schedules the LDC to bid optimally in the DA wholesale market. The LDC considered in this study is equipped with smart grid technology and known as SDC (smart distribution company). This SDC contains DERs (distributed energy resources) including dispatchable and non-dispatchable DGs (distributed generators), BES (battery energy storage) and price sensitive consumers. It can also exchange power with upstream network. The optimization framework considers constraints of DGs and BES as well as AC constraints of the distribution network. Moreover, welfare constraints of load are implemented to maintain customers' satisfaction. This framework determines the optimal bidding strategy of the SDC and nodal hourly prices for end consumers simultaneously in an iterative procedure. The BDT (Benders decomposition technique) with strong cuts is applied to simplify the optimization procedure. Finally, the effectiveness of the proposed strategy is evaluated on several case studies using real data from Ontario power market.