A bi-level integrated generation-transmission planning model incorporating the impacts of demand response by operation simulation
Introduction
Power system planning conventionally consists of generation expansion planning (GEP) and transmission expansion planning (TEP) [1]. GEP deals with the expansion of generation resources to serve growing electric power demand, while TEP concerns the expansion of the grid network to meet the requirements of power transmission [2], [3], [4]. These two planning issues tend to be executed separately, since they have not only different decision variables, objective and constraints but also different stakeholders. However, as the problem of renewable (wind and solar, etc.) generation curtailment becomes increasingly serious, it is currently believed that GEP and TEP should be conducted together to optimize energy utilization and improve investment efficiency, even though some power systems have been deregulated [5], [6]. Some scholars have made contributions to this field in recent years. Seddighi and Ahmadi-Javid [1] present a multistage programming model to balance sustainable power generation expansion planning and transmission expansion planning. Aghaei et al. [7] introduce a probabilistic model for generation and transmission expansion planning considering reliability criteria. Moghaddam et al. [8] put forward a coordinated planning model based on interactive and iterative processes between GEP and TEP. Pozo et al. [9] describe a three-level equilibrium model for the expansion of generation and transmission. Rouhani et al. [10] propose a composite generation and transmission expansion model in which the objectives and constraints of GEP and TEP are integrated.
In addition to generation and transmission, load demand is another important part of power systems. Traditionally, the demand side is not considered in planning issues, because the supply-demand balance in power systems is achieved by adjusting supply to meet demand. However, with the development of smart grid, units in the supply side as well as resources in the demand side can be scheduled by the system operator [11]. The concept of Demand Response (DR) appears as “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized” [12]. Based on this definition, DR can be divided into Price Based Demand Response (PBDR) and Incentive Based Demand Response (IBDR) [13]. Refs. [14], [15] carry out theoretical research on DR and conduct case study of PBDR and IBDR. Ref. [16] shows several typical implementations of DR programs in practice. Now that DR is playing an increasingly important role in power systems, its impacts on power system planning cannot be neglected. The reduction of peak load in the target year through DR is able to decrease the capacity of generation expansion and transmission expansion [17], [18]. Against this background, some scholars have executed researches on planning issues incorporating DR. Yuan et al. [17] introduce a resource planning model considering IBDR, in which load curtailment is regarded as an option for replacing generation expansion to meet peak load demand. Li et al. [18] propose a TEP model with IBDR in order to find the optimal trade-off between transmission investment and load curtailment expenses. Choi and Thomas [19] put forward a GEP model incorporating PBDR, in which the electricity demand projection is revised according to the simulated electricity price. Koltsaklis and Georgiadis [20] present an integrated multi-regional long-term GEP model that considers the impacts of PBDR on electricity demand projection adjustments.
These researches have made great contributions to the exploration of incorporating DR in planning issues. However, more improvements need to be made, since this is still a novel and growing area. First of all, most work in this area only considers the impact of one type of DR, i.e. IBDR or PBDR, instead of considering them together. In addition, researchers tend to only take into account the effects of load curtailment while ignoring the influences of load shifting. For PBDR, most scholars focus on the forecast revision of total electricity demand rather than load profile. More importantly, DR is essentially a concept at the operation level rather than the planning level [21]. That is why more researches and models incorporating DR are in the area of operation issues, such as unit commitment and distributed dispatch [15], [22], [23], [24], [25], [26]. When it comes to planning issues considering DR, existing researches often focus on the effect of DR on reducing peak load [17], [18]. Usually, the available DR capacity at the moment when peak load appears is subtracted from the value of peak load. However, this approach tends to overestimate the effects of DR, because the load demand at another moment may actually become the new peak load after the comprehensive impacts of DR are considered [27]. This new peak load effect typically occurs in two situations that will be analyzed in detail in the following section of this paper.
Based on this background, this paper proposes a bi-level integrated Generation-Transmission Expansion Planning (GTEP) model incorporating the impacts of DR. The elements in generation side, transmission part and demand side are all taken into account simultaneously. The upper level of the model solves the planning problem. The lower level deals with the modified Unit Commitment (UC) problem considering IBDR and the process of PBDR to simulate system operation on the peak load day. The comprehensive effects of DR on actual peak load are reflected through the observation of its cross-hour influences. By virtue of the iterative interaction between the two levels of the model, a reasonably optimal solution can thus be found.
The main contributions and the most salient features of this paper include: (i) all the elements in power supply side, power transmission part and power demand side are considered together, in order to seek the optimal solution from the perspective of the whole power system; (ii) the influences of both IBDR and PBDR are considered in our planning model; (iii) UC problem is incorporated so that the proposed model is a two-level model, in which the actual comprehensive effects of DR on peak load reduction can be figured out through simulation of the whole peak load day instead of just focusing on the peak load moment; (iv) for IBDR in the model, not only load curtailment but also load shifting are considered; (v) the impacts of PBDR on load profile is reflected via the interactions between UC problem and PBDR process within the lower level of the model.
The rest of this paper is organized as follows. Section 2 discusses the impacts of DR on peak load reduction. Section 3 presents our bi-level integrated GTEP model. Section 4 describes a numerical study with result analysis to test the proposed model. Finally, conclusions are outlined in Section 5.
Section snippets
Introduction to IBDR and PBDR
As stated previously, DR can be divided into two main types, i.e. IBDR and PBDR. A brief introduction to them is offered in this subsection to pave the way for the presentation of our model.
IBDR refers to the changes in electricity consumption in response to incentive payments. It is also known as dispatchable DR, which indicates IBDR can be dispatched like generation units [21]. A number of studies have been carried out to incorporate IBDR in UC models or economic dispatch models [22], [23],
Bi-level integrated GTEP model
In this paper, a bi-level integrated GTEP model is proposed. The upper level solves the planning problem. Its model is introduced in Section 3.1. The lower level deals with the operation simulation of the peak load day in the target year. The UC model is put forward in Section 3.2. The operating procedure of the whole bi-level model including the iterative interaction mechanism is explained in Section 3.3.
Input data and scenario settings
In order to verify the effectiveness of the proposed model, a numerical study is carried out based on the IEEE 30 bus system which is a very common choice in researches on power system planning issues [8], [33], [34], [35], [36], [37]. The system contains 6 generation units, 30 node buses and 41 transmission lines. The first bus node serves as the balance node in the system. The construction time of each unit and transmission line is not considered in this study. The parameters of generation
Conclusion
This paper proposes a bi-level integrated generation-transmission expansion planning model in order to seek an optimal expansion scheme for the whole power system. GEP and TEP are combined together. Moreover, the impacts of DR are taken into account. The planning model incorporates a lower level to simulate the operation of the peak load day in the target year. A modified UC model is utilized at this level. IBDR is considered in the UC model, and there is an interaction mechanism between PBDR
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