Enterprise control assessment for the mitigation of renewable energy by demand side management

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 89-104).The traditional power grid paradigm of centralized and actively controlled power generation facilities serving distributed and passively controlled electrical loads is challenged by the requirements for decarbonization, enhanced reliability and transportation electrification. The power grid will undergo technical, economic and regulatory changes and motivates new control and automation technologies and incentivized Demand Side Management (DSM) to accommodate the intermittent and distributed nature of renewable energy. The first phase of this thesis is an extensive review of existing renewable energy integration study methodologies and their limitations. On the other hand, a newly developed holistic enterprise control assessment method manages the diversity of control solutions and many competing objectives, is case independent, addresses both physical nature as well as enterprise control processes, and is validated by a set of numerical simulations. Another major omission in the majority of integration studies is the demand side resources. Demand Side Management with its ability to allow customers to adjust electricity consumption in response to market signals has often been recognized as an efficient way to mitigate the variable effects of renewable energy as well as to increase system efficiency and reduce system costs. Dispite the recongnized importance of DSM, the academic & industrial literature have taken divergent approaches to DSM implementation. While the popular approach among academia adopts a social welfare maximization formulation, the industrial practice compensates customers according to their load reduction from a predefined electricity consumption baseline that would have occurred without DSM. This thesis then rigorously compares the two different DSM approaches in a dayahead electricity wholesale market analytically and numerically using the same system configuration and mathematical formalism. The comparison of the two models showed that a proper reconciliation of the two models might make them mitigate the stochastic netload in fundamentally the same way given an industrial baseline equal to the dispatchable demand forecast in the social welfare model, which is rarely met in practice. While the social welfare model uses a stochastic net load composed of two terms, the industrial DSM model uses a stochastic net load composed of three terms including the additional baseline term. DSM participants have the incentives to manipulate the baseline in order to receive greater financial compensation, taking advantage of greater awareness of their facilities than the regulatory agencies charged with estimating the baseline. In a day-ahead wholesale market, the artificially inflated baseline forecast used in the industrial formalism is shown to result in higher and costlier dispatchable resources scheduling and unachievable social welfare compared to the academic method. This thesis proceeds to compare the two DSM approaches and quantifies the technical impact of industrial baseline errors in subsequent layers of control using an enterprise control methodology. The baseline inflation errors in a day-ahead market have to be corrected in the downstream enterprise control activities at faster time scales, increasing the control efforts and reserve requirements in the real-time market dispatch and regulation service respectively. The adoption of enterprise control simulator added with a dispatchable demand module enables the simultaneous study of day-ahead and real-time market, regulation service and power flow analysis. The day-ahead wholesale market adopts a unit commitment problem and the real-time wholesale market adopts an economic dispatch (ED) problem on the timescale of minutes. While baseline error is absent in the social welfare model, the industrial model is simulated with different baseline levels, assuming the baseline inflation has the same effects in the day-ahead and real-time market. The resulting implications of baseline errors on power grid imbalances and regulating reserve requirements are tracked. It is concluded that with the same regulating service, the introduction of baseline error leads to additional system imbalance compared to the social welfare model results, and the imbalance amplifies itself as the baseline error increases. As a result, more regulating reserves are required to achieve the same satisfactory system performance with higher baseline error. In summary, the industrial DSM baseline inflation brings about higher and costlier dispatch in day-ahead wholesale market and higher reserve requirements in subsequent control layers, namely the real-time market regulating service.by Bo Jiang.S.M