Optimal use of energy storage systems with renewable energy sources

a b s t r a c t This article proposes a multi-period optimization to study the technical and economic effects of the placement and use of Renewable Energy Sources (RES) and Energy Storage Systems (ESS) in an electrical network. As the RES penetrations increase, their inherent variability affects the actual amounts of energy dispatched, their contribution to decrease emissions of pollutants and greenhouse gases, and the overall welfare effects they may have. Moreover, to better harness the energy from renewable sources, both new methodologies and technologies need to be adopted, counteracting the variability and uncertainty of these sources. A possible solution to the challenges of RES adoption is the coupling to energy storage sources, either as dedicated facilities on the supply side, or supporting the accommodation of loads to the available generation on the demand side. This paper suggests an algorithm for network dispatch, aimed at answering some of fundamental changes in the way the system is managed and discusses analytical characteristics of the optimal solution. The proposed methodology is applied to a case study. Four scenarios are analyzed in their dispatches, estimating the welfare effects on the participants in the wholesale market for a modified IEEE 30-bus network with wind energy as the RES in penetrations close to 15%. The policy implications from the results obtained prove that, first, ESS can decrease the ramping necessary for load following, but not necessarily increase the amount of wind energy used, and second, congestion patterns in the electrical network play a crucial role in the final effectiveness of the RES and ESS. These are important insights into an ongoing debate on how to direct storage and renewable energy investments for a low carbon economy

Virtual Power Plant Day Ahead Energy Unit Commitment

In this article we present a model for the interaction of distributed energy resources (DER) with the electricity system, using reinforcement learning. Our method relaxes the requirements for information necessary to train and engage in Pareto improving trading, and can directly incorporate the inherent intermittency of variable renewable energy sources. The distributed resources include consumers of electricity, energy storage systems, and variable renewable energy. We modify the algorithms to improve the scheduling of the resources. In our empirical application, we use data from Colombia subject to large variability due to El Niño Southern Oscillation and illustrate the use of the model under large variations in the data used to train the model

The Case for a Simple Two-Sided Electricity Market

This paper builds on the results from our earlier research on the design of electricity markets that have to accommodate the uncertainty associated with high penetrations of renewable sources of energy. The key results show that 1) distributed storage (deferrable demand) is an effective way to reduce total system costs, 2) a simple market structure for energy allows aggregators to meet their customers\u27 energy needs and provide ramping services to the system operator, and 3) using a receding-horizon optimization to dispatch units for the next market time-step benefits from the availability of more accurate forecasts of renewable generation and allows market participants to adjust their bids and offers in response to this new information. In our two-sided market, distributed storage in the form of deferrable demand is controlled locally by independent aggregators to minimize their expected payments for energy in the wholesale market, subject to meeting the energy needs of their customers. In addition, these aggregators are responsible for maintaining a stable power factor by installing local capabilities that automatically deal with local power imbalances. Failure to do this triggers penalties paid to the system operator. \ \ Our earlier results have shown that it is optimal for an aggregator to submit demand bids into a day-ahead market that include threshold prices for charging and discharging storage and also ensure that the expected amount of stored energy is consistent with the capacity limits of their storage. Because departures from the expected daily pattern of renewable generation are generally persistent (highly positive serial correlated), it is likely that the system operator determines an optimum pattern of demand for the aggregator that violates the capacity limits of storage by the end of the 24-hour period. If the market uses a receding horizon, the results in this paper show that aggregators can modify their bids to ensure that the capacity limits of storage are never violated in the next market time-step. \ \ In an empirical application, a stochastic form of multi-period security constrained unit commitment with optimal power flow (the MATPOWER Optimal Scheduling Tool, MOST) using a receding-horizon optimization determines the optimum dispatch and reserves for the next hour and forecasts of the nodal prices for the next 24 hours. The results show that locally controlled deferrable demand is almost as effective as centrally controlled deferrable demand as a way to reduce system costs and mitigate the variability of renewable generation. The additional advantage from using a receding horizon is that the system operator always charges/discharges the storage managed locally by aggregators within the capacity constraints of the storage

Shale Gas vs. Coal

Abstract: The aim of this paper is to examine the environmental impacts of shale gas, conventional gas and coal on air, water, and land in the United States. These factors decisively affect the quality of life (public health and safety) as well as local and global environmental protection. Comparing various lifecycle assessments, this paper will suggest that a shift from coal to shale gas would benefit public health, the safety of workers, local environmental protection, water consumption, and the land surface. Most likely, shale gas also comes with a smaller GHG footprint than coal. However, shale gas extraction can affect water safety. This paper also discusses related aspects that exemplify how shale gas can be more beneficial in the short and long term. First, there are technical solutions readily available to fix the most crucial problems of shale gas extraction, such as methane leakages and other geo-hazards. Second, shale gas is best equipped to smoothen the transition to an age of renewable energy. Finally, this paper will recommend tighter regulations

Uncertainty-Informed Renewable Energy Scheduling: A Scalable Bilevel Framework

Accommodating the uncertainty of variable renewable energy sources (VRES) in electricity markets requires sophisticated and scalable tools to achieve market efficiency. To account for the uncertain imbalance costs in the real-time market while remaining compatible with the existing sequential market-clearing structure, our work adopts an uncertainty-informed adjustment toward the VRES contract quantity scheduled in the day-ahead market. This mechanism requires solving a bilevel problem, which is computationally challenging for practical large-scale systems. To improve the scalability, we propose a technique based on strong duality and McCormick envelopes, which relaxes the original problem to linear programming. We conduct numerical studies on both IEEE 118-bus and 1814-bus NYISO systems. Results show that the proposed relaxation can achieve good performance in accuracy (0.7%-gap in the system cost wrt. the least-cost stochastic clearing benchmark) and scalability (solving the NYISO system in minutes). Furthermore, the benefit of the uncertainty-informed VRES-quantity adjustment is more significant under higher levels of VRES (e.g., 70%), under which the system cost can be reduced substantially compared to a myopic day-ahead offer strategy of VRES.Comment: Submitted to IEEE PES general meeting 202