Power distribution network design considering dynamic and differential pricing, buy-back, and carbon trading

Highlights

• Consider carbon trading and buy-back policy.

• Determine the generation capacity of the DRER to be installed.

• Determine the differential prices offered to prosumers and consumers.

• Determine the buy-back price.

• A scenario-based stochastic and fuzzy solution approach is provided.

Abstract

The increase in energy demand and the effect of traditional energy generation supported by the penetration of technological advancements in renewable energy generation calls for an integrated power distribution network design. In this study, a power distribution network design with the establishment of distributed renewable energy resources and involvement of prosumers in energy system to generate, buy, and supply carbon-free energy is considered. The dynamic prices that cope with the dynamic demand of both prosumers and customers over the energy planning time horizon and the buy-back price to be paid to the prosumers are also considered. A mathematical model for power distribution network design, including carbon trading and a buy-back policy, was developed. The objective is to determine the generation capacity of the distributed renewable energy resources to be installed at different potential sites, the differential prices offered to prosumers and consumers, and the buy-back price while maximizing the profit of a power plant. A scenario-based stochastic solution approach and fuzzifying fuzzy parameters are provided to address the above problem. The results show that the power plant earns the most profit at a relation factor of 0.8 between the differential prices by offering the highest buy-back price to incentivize the prosumers. With the increase in the relation factor, the net profit of the power company decreases, but the buy-back price increases, while the dynamic prices remain unpredictable. The average buy-back price offered to prosumers was 62.4% of the average selling price.

Introduction

Current studies show that there is a twined-pressure on both energy and environment, an increase in energy demand, and the traditional energy generation sector affects the environment (Yin et al., 2020). To solve such a challenge in the power distribution network design (PDND), there is an increase in the penetration of the distributed renewable energy resources (DRER) in to the PDND supported by the high technological developments in the renewable energy sector (Razavi et al., 2019, Vaccaro et al., 2019). Motivated by the ongoing penetration of DRER by both the power companies and the prosumers in the energy supply system and the overall trend of the energy pricing process by the power plants, this study will focus on integrating dynamic and differential pricing with buy-back pricing and carbon trading.

The rapid growth of energy technologies has fostered an increase in the distributed energy generation. Researches show that the energy generation capacity of solar photovoltaics and wind turbines have increased globally by factors of 26 and 4.5 respectively between the years 2008 and 2018. The use of DRERs has also helped to meet the growing energy demand and address environmental problems (Hahnel et al., 2020, Kuznetsova and Anjos, 2020, Lu et al., 2019). Reports show that 11% of the total power consumption and 17% of energy generation in the United States in 2018 was covered by renewable energy (Chen et al., 2020, US-EIA, 2020). With the evolution of new technologies, energy systems are now becoming more decentralized, enhancing distributed generation close to power consumers, and transforming them from traditional consumers to prosumers (Brown et al., 2020, Jiang et al., 2020). Prosumers are active energy citizens that simultaneously produce and consume energy. They generally participate actively in power systems by investing in the establishment of DRERs and consuming and selling energy (Campos and Marín-González, 2020, Jiang et al., 2021, Jiang et al., 2020, Yin et al., 2020). Research indicates that the involvement of prosumers in the energy system network has two reasons: a constant increase in electricity bills and rapid and massive deployment of solar power panels with a decreasing cost (Kuznetsova & Anjos, 2020). Because they produce and consume power, they affect the pricing schemes in energy distribution network systems (Hua et al., 2020). Current prosumer behavior is characterized by buying additional energy when the capacity of their energy production is less than their consumption demand, or selling energy to a power plant or its neighbors when they produce more energy than their consumption demand (Yin et al., 2020).

Prosumers can be involved in the energy market in one of two ways: by integrating themselves with operators that have an active distribution network, or forming an alliance with other prosumers and creating a virtual power plant (Yin et al., 2020). The involvement of DRER and prosumers in the PDND helps to decentralize the energy market (Guerrero et al., 2020). Prosumers are expected to generate up to 5 MW energy, and 0.5 MW is the point used to classify between superior prosumers and inferior prosumers (Yin et al., 2020). There are two ways of encouraging the generation of renewable energy: The first is to establish DRER units by power plants, while the second is to inspire consumers to install energy generation technologies and play their role in the generation of renewable energy as prosumers and participate in the energy market (Guerrero et al., 2020, Xiao et al., 2020). Power plants can encourage prosumers to be involved in power generation by buying the surplus energy produced by them (Tsao & Vu, 2019). Prosumers in the PDND are believed to play an important role in the flexibility of the power load and changes in the voltage profile (Donaldson and Jayaweera, 2020, Jiang et al., 2021). Studies also indicate that prosumers can reduce the need for extensive power transmission and operating funds. Hence, their involvement in the energy system to support local trade and absorb distributed renewable generation is important (Chen et al., 2020).

Lin et al. (2017) studied the effect of selling back overbooked space in air cargo, they showed that the buy-back creates a win–win situation for the two parties (air cargo and its customer) involved in the network. Such a study can be applied to the PDND with a similar aim to decide whether or not to buy-back and how much to buy-back, and more importantly, it helps both parties enhance their revenues (Lin et al., 2017, Mehrabani and Seifi, 2021, Wee et al., 2013). It is challenging to predict and improve energy pricing accurately. These characteristics arise from its inherent dynamic and nonlinear nature (Jianwei et al., 2019, Mehrabani and Seifi, 2021). Also, energy pricing can vary, which different prices are offered to different customer groups. Conversely, energy pricing can be dynamic if the price offered to a specific customer varies within power consumption periods based on the customer’s time-varying power consumption demand (Tsao & Vu, 2019). Price discrimination offers dynamic pricing during a dynamic power supply period, and differential pricing to several customers based on their demands can generally be called price discrimination (Simshauser, 2018, Wang et al., 2013).

Carbon emission control was a great concern in several countries for many years (Leach, 1991, Worrell et al., 2001), zero-carbon technologies were proposed in high carbon emission sectors like transportation (Schafer & Victor, 1999) and energy agreements were made to alter the increasing trend of CO₂ emissions (Edmonds et al., 1995). Javed et al. (2020) stated that China, United States and India contribute nearly half (50.3%) of the world’s carbon emission in 2018. Energy consumption is expected to increase by 53% in 2035 globally (Javed et al., 2020) which call for more energy generation. The energy sector is also among the service sectors of high carbon emission (Krackeler et al., 1998, Moutinho and Madaleno, 2022, Tucker, 1995) that need great focus. To handle the carbon emission effects, countries plan to take several corrective measures. USA have planned to stabilize carbon emission by planting trees in the years 1990 to 2030 (ROSENTHAL et al., 1993) and implementing energy-efficient technologies (Koomey et al., 1998). China, for example, have planned to decrease its national carbon emissions by 60% to 65% by 2030 (Qin et al., 2020). One of the most important factors for enhancing sustainable energy systems is the control of carbon emissions (Tsao & Thanh, 2020) to address the environmental issue. In addition to providing safer and more reliable energy, DRERs can significantly decrease carbon emissions (Lopez et al., 2020). Hence, power plants should be responsible for controlling carbon emissions and enhancing renewable energy. While considering the growth of power generation, the reduction of carbon emissions, and carbon emission costs, carbon trading is considered as an important market tool. The carbon trading policy also initiates the involvement of renewable energy generators (Biswas et al., 2021, Yavari and Ajalli, 2021, Zhang et al., 2020).

Naturally, demand is uncertain and affects network design (Nahr et al., 2020). There are several uncertainties in the real world and PDND, such as stochastic uncertainty, probabilistic uncertainty, and fuzzy uncertainty (Gholizadeh et al., 2020). The consideration of uncertain parameters can be addressed by a scenario-based stochastic approach (Khalilabadi et al., 2020, Slama et al., 2019) to optimize the objective and help decision makers to make nearly accurate decisions (Nikzad et al., 2019). Uncertain parameters are usually represented by scenarios with discrete probabilities (Hu and Hu, 2018, Sabet et al., 2020). To entertain the fuzzy parameters included in a model, a fuzzy method introduced in the 1970 s (Clark & Kandel, 1991) can also be integrated into the stochastic approach (Tsao & Thanh, 2020).

To the best of the researchers’ knowledge, the literature lacks studies that consider dynamic pricing and differential pricing in an integrated manner in the PDND (Tsao et al., 2019, Tsao and Vu, 2019). Additionally, the buy-back price (BBP) decisions to harvest energy from prosumers need to be addressed. The emergence of prosumers in local renewable energy generation and management, motivated by self-consumption and sales of surplus energy is important because it resolves both environmental issues social issues. This also helps power plants to harvest carbon-free energy from prosumers, allowing them to participate actively in carbon trading. Hence, in this study, we aim attempt to address the gap in the literature by including the buy-back policy in the differential and dynamic pricing of PDND.

The contributions of this study are summarized as follows: First, the developed PDND model integrates differential pricing and dynamic pricing in the power management system. In the literature, to the best of the researchers’ knowledge, differential pricing and dynamic pricing are treated separately in the energy distribution system. In the current model, both differential pricing and dynamic pricing are integrated to address the differential and dynamic nature of demand. Second, the buy-back policy consideration that mutually incentivizes both the power plant and the prosumers is another contribution of the model in this study. The power plant is incentivized by the gain from carbon trading by harvesting renewable energy from prosumers. Prosumers are also incentivized by collecting revenue from the surplus energy generated by managing their dynamic power consumption demand in response to the dynamic prices offered.

The rest of this paper is organized as follows. Section II presents the formulation of the problem and mathematical model development. Section III presents the solution approach used to solve the mathematical model. The numerical analysis and sensitivity analysis are addressed in Sections IV and V, respectively. In Section VI, the concluding remarks of the study are presented.