Aramazd Muzhikyan

An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances—Part II: Methodology

In recent years, an extensive academic and industrial literature has been developed to determine how much such variable energy resources (VERs) may be integrated and how to best mitigate their impacts. While certainly insightful within the context of their application, many integration studies have methodological limitations in that they are case specific, address a single control function of power grid balancing operations, and are often not validated by simulation. This paper presents a holistic method for the assessment of power grid imbalances induced by VERs based upon the concept of enterprise control. It consists within a single package a three-layer enterprise control simulator which includes most of the balancing operation functionality found in traditional power systems. The control layers include a resource scheduling layer composed of a security-constrained unit commitment, a balancing layer composed of a security-constrained economic dispatch, and a regulation layer. The proposed method is validated by a set of numerical simulations. The sequel to this paper submitted to the same issue provides a set of extensive results that demonstrate how power grid balancing operations systematically address VER integration.

An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances—Part II: Parametric Sensitivity Analysis

In recent years, renewable energy has developed to address energy security and climate change drivers. However, as energy resources, they possess a variable and uncertain nature that significantly complicates grid balancing operations. As a result, an extensive academic and industrial literature has developed to determine how much such variable energy resources (VERs) may be integrated and how to best mitigate their impacts. While certainly insightful with the context of their application, many integration studies have methodological limitations because they are case specific, address a single control function of the power grid balancing operations, and are often not validated by simulation. The prequel to this paper presented a holistic method for the assessment of power grid imbalances induced by VERs based upon the concept of enterprise control. This paper now systematically studies these power grid imbalances in terms of five independent variables: 1) day-ahead market time step; 2) real-time market time step; 3) VER normalized variability; 4) normalized day-ahead VER forecast error; and 5) normalized short-term VER forecast error. The systematic study elucidates the impacts of these variables and provides significant insights as to how planners should address these independent variables in the future.

An enhanced method for the determination of load following reserves

Power generation reserves play a central role for maintaining the balance of generation and consumption. Reserves, scheduled in advance, compensate for forecast error, variability and transmission losses. However, as reserves are a costly commodity, their amount should be carefully assessed to prevent unnecessary expense. Currently, the quantity of required reserves are determined based upon a posteriori methods that use operators experience and established assumptions. This paper instead presents a method founded upon non-dimensional numbers and digitial signal processing to determine the quantity of load following reserves a priori.

Impacts of industrial baseline errors in demand side management enabled enterprise control

Despite the recognized importance of demand side management (DSM) for mitigating the impact of variable energy resources and reducing the system costs, the academic and industrial literature have taken divergent approaches to DSM implementation. The prequel to this work has demonstrated that the inflation of the net load baseline forecast, used by the industrial unit commitment formulation, leads to higher and costlier day-ahead scheduling of dispatchable resources compared to the academic method. Consequently, these baseline inflation errors have to be corrected in the downstream enterprise control activities at faster time scales, increasing the control efforts and reserve requirements for the real-time market dispatch and regulation service. This paper compares the two DSM approaches and quantifies the technical impact of industrial baseline errors in subsequent layers of control using an enterprise control methodology. The adopted enterprise control simulator encompasses three interconnected layers: a resource scheduling layer composed of a security-constrained unit commitment (SCUC), a balancing layer composed of a security-constrained economic dispatch (SCED), and a regulation layer. Baseline error is absent in the social welfare model. The simulations with the industrial model are run for different baseline error levels. The baseline inflation is assumed to have 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.

An enhanced method for the determination of the ramping reserves

Power generation reserves play a central role for maintaining the balance of generation and consumption. Reserves, scheduled in advance, compensate for forecast error, variability and transmission losses. However, as reserves are a costly commodity, their amount should be carefully assessed to prevent unnecessary expense. Currently, the quantity of required reserves are determined based upon a posteriori methods that use operators experience and established assumptions. While these assumptions have been made out of a level of engineering practicality, they may not be formally true given the numerical evidence. The earlier “sister” paper to this work presented a method to determine the quantity of load following reserves a priori. This paper now uses a similar methodology to determine the quantity of ramping reserves.

An enhanced method for the determination of the regulation reserves

Generation reserves are the key resource for balancing power system generation and consumption. Reserves are used to mitigate any imbalances due to uncertainty and variability. However, as a costly commodity, the amount of reserves should be carefully assessed to maintain the cost of power system operations at its minimum. Currently, reserve requirement determination is based upon a posteriori methods that use the experience of power system operators and established assumptions. While these assumptions have been made out of a level of engineering practicality, they may not be formally true given the numerical evidence. The two prequels to this work presented methods for determination of the load following and ramping reserve requirements a priori. This paper now uses a similar methodology to determine the regulation reserve requirement.

The impact of wind power geographical smoothing on operating reserve requirements

As one of the common sources of renewable energy generation worldwide, wind power is the subject of numerous academic and industrial studies. The impact of the wind generation on different aspects of power system operations is extensively addressed in the literature. One of the important aspects of such studies is related to the dynamics of the wind generation that emerges when the wind turbines are arranged into arrays. Arranging wind turbines into arrays alters the output wind generation due to the emerging coupling between wind turbines and the geographical smoothing. While the impact of the coupling between wind turbines on the output magnitudes is well studied, the impact of geographical distribution of turbines on wind power variability has received little attention. Moreover, the impact of the geographical smoothing on the operating reserve requirements is omitted. This paper develops a mathematical framework that relates the variability of the wind farm output to the geographical distribution of wind turbines. The proposed method is used to study the impact of the geographical distribution on the requirements of three types of operating reserves, namely, load following, ramping and regulation. The results show that the geographical distribution reduces all three types of operating reserve requirements.