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Abstract Decarbonizing the electricity sector requires massive investments in generation and transmission infrastructures that may impact both water and land resources. Characterizing these effects is key to ensure a sustainable energy transition. Here, we identify and quantify the unintended consequences of decarbonizing the China Southern Power Grid, China’s second-largest grid. We show that reaching carbon neutrality by 2060 is feasible; yet, doing so requires converting 40,000 square kilometers of land to support solar and wind as well as tapping on rivers to build ~32 gigawatts of hydropower. The impact of wind and solar development would span across multiple sectors, since crop and grassland constitute 90% of the identified sites. The construction of new dams may carry major externalities and trickle down to nearby countries, as most dams are located in transboundary rivers. Curbing the international footprint of this decarbonization effort would require additional investments (~12 billion United States dollars) in carbon capture technologies.

Chinese electricity market reform poses huge challenges to hydropower operations and electricity trading. This study proposes a scheduling method coupling priority electricity and day-ahead trading for large hydropower plants. The study focuses on complex factors such as tariff uncertainty, different types of electricity settlement rules, and inter-provincial electricity transmission links. Spot market tariff scenarios are determined through the Latin hypercube and the K-means methods. A performance formulation of priority electricity deviation considering settlement assessment rules is established. A transmission description for different sub-plants and a triangular linear interpolation method based on binary independent branching mode are proposed to solve inter-regional transmission connections and hydraulic coupling in cascaded hydropower plants, respectively. Finally, the Big M method is employed to equivalently transform the complex non-linear problem into a mixed-integer linear programming (MILP) model. The method is verified with the day-ahead operation of four large hydropower plants downstream of the Jinsha River in China as a case study. Settlement assessment rules, inter-regional power transmission, and price uncertainty are analyzed in three different cases. Three conclusions are obtained: 1) the priority electricity performance rate and the price are positively correlated, which is useful to guide hydropower plants to actively participate in the market. 2) Introducing the prediction error of electricity price in the model can help avoid market decision risk and improve the expected return by approximately 1.2%. 3) Considering the settlement penalty rule is helpful for power generation enterprises to improve power allocation and thus seek higher revenue compared to traditional methods without considering it.

Abstract Mixed‐integer linear programming is widely used in hydropower reservoirs operation optimisation because of its modelling flexibility, solution stability, and global search capability. A major obstacle to applying mixed‐integer linear programming to hydropower reservoirs operation optimisation, especially for long‐term operations of large‐scale hydropower systems, is the non‐linear phenomenon while converting water energy into electricity, which is normally expressed as a non‐linear bivariate function, that is, hydropower production function. To cross this technological barrier, a novel linearisation method for hydropower production function is proposed. The method uses rectangular meshing techniques to approximate hydropower production function. Then Special Ordered Sets of type 2 (SOS2)constraints are adopted for sub‐rectangle selection, which are formulated based on the binary branching schemes generated by binary reflected grey code. The proposed method can linearise the hydropower production function with only logarithmic‐sized binary variables of the traditional methods and maintain high accuracy. The method is then applied to optimising the operation of the Lancang River Basin hydropower system, which has 13 head‐dependent reservoirs with a deterministic model and a two‐stage stochastic model. The results show that the authors' method significantly outperforms conventional linearisation methods and can obtain accurate solutions within an acceptable time for deterministic and stochastic large‐scale hydropower reservoirs operation optimisation problems.

Uncertainties from neighboring rival’s reservoirs challenge hydropower companies in participating in competitive markets. Cooperative behaviors are generally impractical due to stakeholders’ self-interest and regulatory requirements. Considering this obstacle, this paper proposes a data-driven bilevel model, in a competitive context, to estimate the operational information of the neighboring rival’s reservoir, including its historical operating states and operational functions. The proposed bilevel model is an inverse problem of the conventional hydropower scheduling model. The upper-level model is designed to find the most appropriate operational parameters of the estimated reservoir that fit its historical generation volumes. The lower-level model simulates the profit-maxing operation of the estimated reservoir. Since the lower simulating model is nonconvex, an Enhanced Parallel Genetic Algorithm (EPGA) is proposed. It avoids infeasible situations through several strategies and uses multiple CPU threads simultaneously in solving. A case study in China’s market demonstrates that the proposed model and solving method can efficiently obtain accurate state series and (near-)optimal operational parameters. More experiments are also taken to validate the parallel design.

Taking into account both market benefits and power grid demand is one of the main challenges for cascade hydropower stations trading on electricity markets and secluding operation plan. This study develops a multi-objective optimal operation model for the long-term operation of cascade hydropower in different markets. Two objectives were formulated for economics benefits and carryover energy storage. One was to maximize the market utility value based on portfolio theory, for which conditional value at risk (CVaR) was applied to measure the risk of multi-markets. Another was the maximization of energy storage at the end of a year. The model was solved efficiently through a multi-objective particle swarm optimization (MOPSO). Under the framework of the MOPSO, the chaotic mutation search mechanism and elite individual retention mechanism were introduced to diversify and accelerate the non-inferior solution sets. Lastly, a dynamic updating of archives was established to collect the non-inferior solution. The proposed model was implemented on the hydropower plants on the Lancang River, which traded on the Yunnan Electricity Market (YEM). The results demonstrated non-inferior solution sets in wet, normal and dry years. A comparison in solution sets revealed an imbalanced mutual restriction between objectives, such that a 2.65 billion CNY increase in market utility costs a 13.96 billion kWh decrease in energy storage. In addition, the non-inferior solution provided various schemes for actual demands based on other evaluating indexes such as the minimum output, power generation and spillage.

To meet the rapid growth of electricity demand and reduce carbon intensity, China is developing renewable energies rapidly including hydropower, wind and solar power. Due to the geographical mismatch of energy sources and demands in China, many long-distance and large-scale UHVDC and HVDC transmission projects have been built to transmit electric power from the western renewable bases to eastern coastal load centers. Some provincial power sources serve both local demands and deliver power to multiple regional power grids via HVDC transmission lines. As large capacity HVDC power transmission projects have great impacts on receiving-end power grids. Thus, the local exporting power grid should consider both local demands and energy importing area demands. A mixed-integer linear programming day-ahead peak shaving model to minimize the peak-valley difference in residual load after renewable generation of multiple power grids is developed. The model uses chance constraints to compensate for forecast errors of wind and solar power with hydropower, and introduces maximum daily power regulation times and stair-like power curve constraints of HVDC tie lines to avoid frequent HVDC power change and ensure power grid safety. The case studies in Yunnan province, which has large scale hydro, wind and solar power sources and delivers power to multiple regional power grids via HVDC transmission lines, shows the proposed model can shave peaks from multiple power grids effectively, hydropower can compensate for wind and solar forecast error and obtain satisfying results for multiple power grids, and that HVDC constraints can avoid their frequent power change and ensure the power grid safety.

Due to the distribution of runoff is uneven and uncertain in time and space, there are huge differences between the electricity markets dominated by hydropower and thermal power. Therefore, how to evaluate the risk of cascade plants participating in electricity market is an urgent problem to be solved. Based on the rules of the market in southwest China which hydropower plays a leading role, this paper proposes a risk analysis method for cascade hydropower to participate in market which is coupled with monthly market and day-ahead market: Copula-Monte Carlo is used to generate the combined scenario of daily runoff and daily clearing price, and the LINGO solver is used to calculate the generation income of all scenarios, and the corresponding conditional value-at-risk value(CVaR) of portfolio is obtained according to the confidence level. The method takes into account the uncertainty of runoff and electricity price in the meantime, as well as the settlement order and deviation assessment of each market in the settlement rules, and can directly solve the income and risk value. The process is simple and the physical meaning is clear. Using the actual data of a grid cascade hydropower participating in the electricity market as an example, the simulation analysis of the results shows that the proposed method can easily solve the portfolio risk value, and can more reasonably evaluate the risk value compared with the considering runoff and day-ahead market clearing prices separately.

Some hydropower reservoirs are operated under different constraint levels. For these reservoirs, a multi-level (ML) dependent-chance (DC) model is established. In the model, only when the higher-level constraints are satisfied are the lower-level constraints or system benefits considered. The multi-level dependent-chance (MLDC) model is specified by two models. One is based on existing reliability-constrained (RC) dynamic programming (DP), in which the soft constraints are addressed using reliability constraints of 1, and the priorities are reflected using the order of magnitudes of Lagrange multipliers. The other is the explicit dependent-chance reasoning in the DP recursive function, in which each soft constraint is represented as an objective function of negative expected failure time and the optimum is the solution with a larger value for all higher-level objective functions. The proposed models are applied to derive long-term operation rules for the hydropower system on the middle-lower Lancang River. The results show the feasibility and performances of the explicit graded constraint control of the proposed model and the solution methods.

Long-distance and large-capacity hydropower transmission from large hydropower plants in southwestern China to load centers is an important and effective measure for the accommodation of large-scale hydropower in China. However, the load demands of the receiving-end power grids are not fully considered in the conventional power transmission mode, which has greatly affected their enthusiasm for the absorption of trans-regional hydro energy. In order to make full use of the peak shaving capability of the hydropower plants, this paper develops an optimization model for determining the hourly generation scheduling of the hydropower plants transmitting electric power to several power grids via high voltage direct current (HVDC) transmission lines. A large-capacity and highly complex multi-unit hydropower system, the Xiluodu plant in China, is taken as the case study. In the proposed model, minimizing the peak-valley differences of multiple receiving-end power grids is adopted as the objective to alleviate the peak shaving pressure of the receiving-end power grids. In addition to the traditional hydraulic constraints, the operation constraints of individual units and HVDC power transmission limits are well considered. The study focuses mainly on modeling the stair-like power transmission curve constraint which is discrete and nonlinear and has been rarely considered in previous studies. This constraint is then linearized through limiting the logical relations between multiple binary integer variables. Case studies demonstrate that the proposed model has high computational efficiency, and the peak-valley differences of the two receiving-end power grids, i.e., Zhejiang Power Grid and Guangdong Power Grid, are decreased by 15.2% and 7.33%, respectively. Moreover, frequent conversion of HVDC converter equipment can also be avoided, which makes the obtained generation schedule more executable.

In medium-term scheduling, the end-of-term storage energy maximization model is proposed to create conditions for the safety, stability and economic operation of the hydropower system after control term, which satisfies the system load demand undertaken by the cascaded system in a given scheduling period. This paper presents a simplified solution method based on the Lagrangian relaxation method (LR) to solve the end-of-term storage energy maximization model. The original Lagrange dual problem with multiple Lagrange multipliers is converted to that with only one Lagrange multiplier by an entropy-based aggregate function method, which relaxes the complex cascaded hydropower system load balance constraints. The subgradient method and successive approximation of dynamic programming (DPSA) are adopted to update the Lagrange multiplier iteratively and solve the subproblem of the Lagrange dual problem, respectively. The Wujiang cascaded hydropower system is studied, and the result shows that the simplified solution method for the end-of-term storage energy maximization model both improves solving efficiency and ensures solving accuracy to a great extent.

Abstract Power flow transfer (PFT) analysis under various anticipated faults in advance is important for securing power system operations. In China, PSD-BPA software is the most widely used tool for power system analysis, but its input/output interface is easily adapted for PFT analysis, which is also difficult due to its computationally intensity. To solve this issue, and achieve a fast and accurate PFT analysis, a modular parallelization framework is developed in this paper. Two major contributions are included. One is several integrated PFT analysis modules, including parameter initialization, fault setting, network integrity detection, reasonableness identification and result analysis. The other is a parallelization technique for enhancing computation efficiency using a Fork/Join framework. The proposed framework has been tested and validated by the IEEE 39 bus reference power system. Furthermore, it has been applied to a practical power network with 11052 buses and 12487 branches in the Yunnan Power Grid of China, providing decision support for large-scale power system analysis.

China is implementing a new power system reform, with one goal of renewable energy absorption such as hydropower. However, the forthcoming spot market challenges cascade hydropower generation in terms of the short-term hydro scheduling (STHS) problem. Specifically, STHS involves fulfilling bilateral market obligations and bidding for the day-ahead market with uncertainty. Coordination of these two tasks while managing market risks becomes a problem that must be urgently solved. Herein, we propose a method based on the information-gap decision theory (IGDT) to solve the cascade hydropower STHS problem, wherein the aforementioned tasks are coordinated simultaneously. The IGDT method was used to deal with the uncertainty of the day-ahead market price, and the robustness function was derived. A mixed-integer nonlinear programming model was used to describe the proposed problem, and a commercial solver was used to solve it. A four-reservoir cascade hydropower company was used as the research object. Through the robust dispatching results, the preset profit objectives of the power generation company were satisfied within the price information gap, and the day-ahead market bidding strategy and daily contract decomposition curve were obtained. The proposed model is found to be superior to the scenario-based probability method. Moreover, a comparative analysis of bilateral contract fulfillment showed that more profits can be obtained by coordinating contract fulfillment in the day-ahead market.

Under the medium- and long-term electric markets, cascaded hydropower stations face a series of practical challenges due to the uncertainty of inflow and market price. For long-term dispatch scheduling, the allocation of power generation in multimarkets is critical, including clean energy priority consumption market, inter provincial market and intra provincial market in order to maximize the operator’s expected revenue and reduce the market operation risks. Based on the hydro-dominant electricity market structure and settlement rules, we propose a long-term optimal operation method for cascade hydropower stations considering the uncertainty of multiple variables. First, a seasonal autoregressive integrated moving average model is used to handle the time-varying and seasonal characteristics of inflow series by using a copula connect function to fit the joint distribution of the monthly inflow, the clearing price of the intra provincial market and the delivery volume of the inter provincial market. Then, uncertain chance-constrained programming is established. Finally, a developed particle swarm optimization algorithm embedded in a Monte Carlo simulation is solved for hydropower operation policies, and the maximum revenue, resource allocation and scheduling strategy are obtained under the corresponding risk tolerance. Taking the actual data of cascaded hydropower stations in Yunnan Province, China, as an example, a simulation analysis is carried out. The results show that the proposed method can reasonably describe the uncertainty and correlation between the variables, realizing the optimal allocation of resources among multimarkets, and provide references for the long-term optimal operation of cascade hydropower stations in a multimarket environment. The results also show that the decision strategies should be determined considering the decision-maker’s risk preference.

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

The bulk hydropower transmission via trans-provincial and trans-regional power networks in China provides great operational flexibility to dispatch power resources between multiple power grids. This is very beneficial to alleviate the tremendous peak load pressure of most provincial power grids. This study places the focus on peak operations of cascaded hydropower plants serving multiple provinces under a regional connected AC/DC network. The objective is to respond to peak loads of multiple provincial power grids simultaneously. A two-stage search method is developed for this problem. In the first stage, a load reconstruction strategy is proposed to combine multiple load curves of power grids into a total load curve. The purpose is to deal with different load features in load magnitudes, peaks and valleys. A mutative-scale optimization method is then used to determine the generation schedules of hydropower plants. In the second stage, an exterior point search method is established to allocate the generation among multiple receiving power grids. This method produces an initial solution using the load shedding algorithm, and further improves it by iteratively coordinating the generation among different power grids. The proposed method was implemented to the operations of cascaded hydropower plants on Xin-Fu River and another on Hongshui River. The optimization results in two cases satisfied the peak demands of receiving provincial power grids. Moreover, the maximum load difference between peak and valley decreased 12.67% and 11.32% in Shanghai Power Grid (SHPG) and Zhejiang Power Grid (ZJPG), exceeding by 4.85% and 6.72% those of the current operational method, respectively. The advantage of the proposed method in alleviating peak-shaving pressure is demonstrated.

The increasing peak-to-valley load difference in China pose a challenge to long-distance and large-capacity hydropower transmission via high-voltage direct current (HVDC) lines. Considering the peak shaving demands of load centers, an optimization model that maximizes the expected power generation revenue is proposed here for the long-term operation of an interprovincial hydropower plant. A simulation-based method was utilized to explore the relationships between long-term power generation and short-term peak shaving revenue in the model. This method generated representative daily load scenarios via cluster analysis and approximated the real-time electricity price of each load profile with the time-of-use price strategy. A mixed-integer linear programming model with HVDC transmission constraints was then established to obtain moving average (MA) price curves that bridged two time-coupled operations. The MA price curves were finally incorporated into the long-term optimization model to determine monthly generation schedules, and the inflow uncertainty was addressed by discretized inflow scenarios. The proposed model was evaluated based on the operation of the Xiluodu hydropower system in China during the drawdown season. The results revealed a trade-off between long-term energy production and short-term peak shaving revenue, and they demonstrated the revenue potential of interprovincial hydropower transmission while meeting peak shaving demands. A comparison with other long-term optimization methods demonstrated the effectiveness and reliability of the proposed model in maximizing power generation revenue.

The integration of large-scale wind and solar power into the power grid brings new challenges to the security and stability of power systems because of the uncertainty and intermittence of wind and solar power. This sensitive expected output has real implications for short-term hydro scheduling (STHS), which provides reserve services to alleviate electrical perturbations from wind and solar power. This paper places an emphasis on the sensitive expected output characteristics of hydro plants and their effect on reserve services. The multi-step progressive optimality algorithm (MSPOA) is used for the STHS problem. An iterative approximation method is proposed to determine the forebay level and output under the condition of the sensitive expected output. Cascaded hydro plants in the Wujiang River are selected as an example. The results illustrate that the method can achieve good performance for peak shaving and reserve services. The proposed approach is both accurate and computationally acceptable so that the obtained hydropower schedules are in accordance with practical circumstances and the reserve can cope with renewable power fluctuations effectively.

Climate change could have dire effects on hydropower systems, especially in southwest China, where hydropower dominates the regional power system. This study examines two large cascade hydropower systems in Yunnan province in southwest China for 10 climate change projections made with 5 global climate models (GCMs) and 2 representative concentration pathways (RCPs) under Coupled Model Intercomparison Project Phase 5 (CMIP5). First, a back propagation neural network rain-runoff model is built for each hydropower station to estimate inflows with climate change. Then, a progressive optimality algorithm maximizes hydropower generation for each projection. The results show generation increasing in each GCM projection, but increasing more in GCMs under scenario RCP8.5. However, yearly generation fluctuates more: generation decreases dramatically with potential for electricity shortages in dry years and more electricity as well as spill during wet years. Average annual spill, average annual inflow and average storage have similar trends. The analysis indicates that a planned large dam on the upper Jinsha River would increase seasonal regulation ability, increase hydropower generation, and decrease spill. Increased turbine capacity increases generation slightly and decreases spill for the Lancang River. Results from this study demonstrate effects of climate change on hydropower systems and identify which watersheds might be more vulnerable, along with some actions that could help adapt to climate change.

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

The large-scale cascade hydropower plants in southwestern China now challenge a multi-market environment in the new round of electricity market reform. They not only have to supply the load for the local provincial market, but also need to deliver electricity to the central and eastern load centers in external markets, which makes the generation scheduling much more complicated, with a correlated uncertain market environment. Considering the uncertainty of prices and correlation between multiple markets, this paper has proposed a novel optimization model of long-term generation scheduling for cascade hydropower plants in multiple markets to seek for the maximization of overall benefits. The Copula function is introduced to describe the correlation of stochastic prices between multiple markets. The price scenarios that obey the Copula fitting function are then generated and further reduced by using a scenario reduction strategy that combines hierarchical clustering and inconsistent values. The proposed model is applied to perform the long-term generation scheduling for the Wu River cascade hydropower plants and achieves an increase of 106.93 million yuan of annual income compared with the conventional scheduling model, without considering price scenarios, showing better performance in effectiveness and robustness in multiple markets.

Due to booming economic development over the past decades, energy demands in most of China’s provincial power grids have increased sharply, and it has become challenging to guarantee the energy balance at peak periods. In many provincial electric systems of China, gas-fired generators are one of the most important peaking power sources to respond the load change at peak periods. To meet this practical necessity, a novel mixed integer linear programming model is proposed in this paper for the peak operation of gas-fired generating units with disjoint-prohibited operating zones. In the developed model, the objective function is chosen to minimize the peak-valley difference of the remaining load series that is obtained by subtracting the total generation of all the gas-fired units from the original load curve. The real-world simulations in several cases show that the developed model is able to generate satisfying scheduling results by reasonably allocating the power outputs of all the gas-fired generators in the scheduling horizon. Then, the management implications obtained lie in the fact that it is necessary to increase the share of peak power sources in the mid- to long-term planning of an electrical power system; and in the daily operation of the power grid, greater flexibility should be given to the gas-fired units to reduce peak pressure.

This paper presents an integrated method to obtain optimal market operation and regulation with the objective of reducing the market price and increasing the electricity consumption in hydro-dominated electricity markets, in which giant cascaded hydropower facilities along different rivers are main power suppliers. To this end, a comprehensive indicator composed of market prices and electricity consumption is proposed to evaluate the situation of hydro-dominated market operation. Moreover, an iterative algorithm is proposed to investigate the strategic behaviors of power suppliers and to simulate the operation of the market. Furthermore, an integrated solution methodology based on a multi-core parallel tabu genetic algorithm (MPTGA) is proposed to provide the optimal assignment of bilateral contracts, considering the market simulation, in order to achieve the optimal market regulation. The results from the case study, with real data based on Yunnan’s electricity market, demonstrate that the proposed indicator and method are effective and efficient to simulate and regulate the market operation, and the effects of MPTGA are discussed last.

For the power systems, for which few data are available for mid-term electricity market clearing price (MCP) forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N) model) is proposed in this paper. Over the traditional GM(0, N) model, three major improvements of the proposed model are: (i) the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii) a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii) the model parameters are identified by an improved particle swarm optimization (PSO) instead of the least square method (LSM) for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR) model, the traditional GM(0, N) model and the artificial neural network (ANN) model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold–Mariano (MDM) test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market.

In this paper, a two-dimensional Cournot model is proposed to study generation companies’ (GENCO’s) strategic quantity-setting behaviors in the newly established Yunnan’s electricity market. A hybrid pricing mechanism is introduced to Yunnan’s electricity market with the aim to stimulate electricity demand. Market equilibrium is obtained by iteratively solving each GENCO’s profit maximization problem and finding their optimal bidding outputs. As the market mechanism is a key element of the electricity market, impacts of different market mechanism parameters on electricity price and power generation in market equilibrium state should be fully assessed. Therefore, based on the proposed model, we precisely explore the impacts on market equilibrium of varying parameters such as the number of GENCOs, the quantity of ex-ante obligatory-use electricity contracts (EOECs) and the elasticity of demand. Numerical analysis results of Yunnan’s electricity market show that these parameters have notable but different effects on electricity price. A larger number of GENCOs or less EOEC contracted with GENCOs will have positive effects on reducing the price. With the increase of demand elasticity, the price falls first and then rises. Comparison of different mechanisms and relationship between different parameters are also analyzed. These results should be of practical interest to market participants or market designers in Yunnan’s or other similar markets.